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İsmail Mendi B, Kose K, Fleshner L, Adam R, Safai B, Farabi B, Atak MF. Artificial Intelligence in the Non-Invasive Detection of Melanoma. Life (Basel) 2024; 14:1602. [PMID: 39768310 PMCID: PMC11678477 DOI: 10.3390/life14121602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2024] [Revised: 11/27/2024] [Accepted: 11/29/2024] [Indexed: 01/05/2025] Open
Abstract
Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.
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Affiliation(s)
- Banu İsmail Mendi
- Department of Dermatology, Niğde Ömer Halisdemir University, Niğde 51000, Turkey
| | - Kivanc Kose
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA;
| | - Lauren Fleshner
- School of Medicine, New York Medical College, Valhalla, NY 10595, USA; (L.F.); (R.A.); (B.S.); (B.F.)
| | - Richard Adam
- School of Medicine, New York Medical College, Valhalla, NY 10595, USA; (L.F.); (R.A.); (B.S.); (B.F.)
| | - Bijan Safai
- School of Medicine, New York Medical College, Valhalla, NY 10595, USA; (L.F.); (R.A.); (B.S.); (B.F.)
- Dermatology Department, NYC Health + Hospital/Metropolitan, New York, NY 10029, USA;
| | - Banu Farabi
- School of Medicine, New York Medical College, Valhalla, NY 10595, USA; (L.F.); (R.A.); (B.S.); (B.F.)
- Dermatology Department, NYC Health + Hospital/Metropolitan, New York, NY 10029, USA;
- Dermatology Department, NYC Health + Hospital/South Brooklyn, Brooklyn, NY 11235, USA
| | - Mehmet Fatih Atak
- Dermatology Department, NYC Health + Hospital/Metropolitan, New York, NY 10029, USA;
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Vardasca R, Mendes JG, Magalhaes C. Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review. J Imaging 2024; 10:265. [PMID: 39590729 PMCID: PMC11595075 DOI: 10.3390/jimaging10110265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Revised: 09/26/2024] [Accepted: 10/17/2024] [Indexed: 11/28/2024] Open
Abstract
The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies are improved. Currently, diagnosis is heavily based on physicians' judgment and experience, which can occasionally lead to the worsening of the lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy or multiphoton laser scanning microscopy, have been explored for skin cancer assessment, which have been aligned with different artificial intelligence (AI) strategies to assist in the diagnostic task, based on several image features, thus making the process more reliable and faster. This systematic review concerns the implementation of AI methods for skin tumor classification with different imaging modalities, following the PRISMA guidelines. In total, 206 records were retrieved and qualitatively analyzed. Diagnostic potential was found for several techniques, particularly for dermoscopy images, with strategies yielding classification results close to perfection. Learning approaches based on support vector machines and artificial neural networks seem to be preferred, with a recent focus on convolutional neural networks. Still, detailed descriptions of training/testing conditions are lacking in some reports, hampering reproduction. The use of AI methods in skin cancer diagnosis is an expanding field, with future work aiming to construct optimal learning approaches and strategies. Ultimately, early detection could be optimized, improving patient outcomes, even in areas where healthcare is scarce.
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Affiliation(s)
- Ricardo Vardasca
- ISLA Santarem, Rua Teixeira Guedes 31, 2000-029 Santarem, Portugal
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Universidade do Porto, 4099-002 Porto, Portugal; (J.G.M.); (C.M.)
| | - Joaquim Gabriel Mendes
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Universidade do Porto, 4099-002 Porto, Portugal; (J.G.M.); (C.M.)
- Faculdade de Engenharia, Universidade do Porto, 4099-002 Porto, Portugal
| | - Carolina Magalhaes
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Universidade do Porto, 4099-002 Porto, Portugal; (J.G.M.); (C.M.)
- Faculdade de Engenharia, Universidade do Porto, 4099-002 Porto, Portugal
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Lboukili I, Stamatas G, Descombes X. DermoGAN: multi-task cycle generative adversarial networks for unsupervised automatic cell identification on in-vivo reflectance confocal microscopy images of the human epidermis. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:086003. [PMID: 39099678 PMCID: PMC11294601 DOI: 10.1117/1.jbo.29.8.086003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/17/2024] [Accepted: 05/30/2024] [Indexed: 08/06/2024]
Abstract
Significance Accurate identification of epidermal cells on reflectance confocal microscopy (RCM) images is important in the study of epidermal architecture and topology of both healthy and diseased skin. However, analysis of these images is currently done manually and therefore time-consuming and subject to human error and inter-expert interpretation. It is also hindered by low image quality due to noise and heterogeneity. Aim We aimed to design an automated pipeline for the analysis of the epidermal structure from RCM images. Approach Two attempts have been made at automatically localizing epidermal cells, called keratinocytes, on RCM images: the first is based on a rotationally symmetric error function mask, and the second on cell morphological features. Here, we propose a dual-task network to automatically identify keratinocytes on RCM images. Each task consists of a cycle generative adversarial network. The first task aims to translate real RCM images into binary images, thus learning the noise and texture model of RCM images, whereas the second task maps Gabor-filtered RCM images into binary images, learning the epidermal structure visible on RCM images. The combination of the two tasks allows one task to constrict the solution space of the other, thus improving overall results. We refine our cell identification by applying the pre-trained StarDist algorithm to detect star-convex shapes, thus closing any incomplete membranes and separating neighboring cells. Results The results are evaluated both on simulated data and manually annotated real RCM data. Accuracy is measured using recall and precision metrics, which is summarized as the F 1 -score. Conclusions We demonstrate that the proposed fully unsupervised method successfully identifies keratinocytes on RCM images of the epidermis, with an accuracy on par with experts' cell identification, is not constrained by limited available annotated data, and can be extended to images acquired using various imaging techniques without retraining.
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Affiliation(s)
- Imane Lboukili
- Johnson & Johnson Santé Beauté France, Paris, France
- UCA, INRIA, I3S/CNRS, Sophia Antipolis, France
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Luo N, Zhong X, Su L, Cheng Z, Ma W, Hao P. Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal. Comput Biol Med 2023; 165:107413. [PMID: 37703714 DOI: 10.1016/j.compbiomed.2023.107413] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 09/15/2023]
Abstract
Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.
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Affiliation(s)
- Nan Luo
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Xiaojing Zhong
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Luxin Su
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Zilin Cheng
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Wenyi Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Pingsheng Hao
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
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Owida HA. Developments and Clinical Applications of Noninvasive Optical Technologies for Skin Cancer Diagnosis. J Skin Cancer 2022; 2022:9218847. [PMID: 36437851 PMCID: PMC9699785 DOI: 10.1155/2022/9218847] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/29/2022] [Accepted: 11/09/2022] [Indexed: 04/03/2024] Open
Abstract
Skin cancer has shown a sharp increase in prevalence over the past few decades and currently accounts for one-third of all cancers diagnosed. The most lethal form of skin cancer is melanoma, which develops in 4% of individuals. The rising prevalence and increased number of fatalities of skin cancer put a significant burden on healthcare resources and the economy. However, early detection and treatment greatly improve survival rates for patients with skin cancer. Since the rising rates of both the incidence and mortality have been particularly noticeable with melanoma, significant resources have been allocated to research aimed at earlier diagnosis and a deeper knowledge of the disease. Dermoscopy, reflectance confocal microscopy, optical coherence tomography, multiphoton-excited fluorescence imaging, and dermatofluorescence are only a few of the optical modalities reviewed here that have been employed to enhance noninvasive diagnosis of skin cancer in recent years. This review article discusses the methodology behind newly emerging noninvasive optical diagnostic technologies, their clinical applications, and advantages and disadvantages of these techniques, as well as the potential for their further advancement in the future.
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Affiliation(s)
- Hamza Abu Owida
- Medical Engineering Department, Faculty of Engineering, Al Ahliyya Amman University, Amman 19328, Jordan
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Zhang S, Wang Y, Zheng Q, Li J, Huang J, Long X. Artificial intelligence in melanoma: A systematic review. J Cosmet Dermatol 2022; 21:5993-6004. [PMID: 36001057 DOI: 10.1111/jocd.15323] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/12/2022] [Accepted: 08/19/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Melanoma accounts for the majority of skin cancer deaths. Artificial intelligence has been applied in many types of cancers, and in melanoma in recent years. However, no systematic review summarized the application of artificial intelligence in melanoma. AIMS This study aims to systematically review previously published articles to explore the application of artificial intelligence in melanoma. MATERIALS & METHODS PubMed database was used to search the eligible publications on August 1, 2020. The query term was "artificial intelligence" and "melanoma." RESULTS A total of 51 articles were included in this review. Artificial intelligence technique is mainly used in the evaluation of dermoscopic images, other image segmentation and processing, and artificial intelligence diagnosis system. DISCUSSION Artificial intelligence is also applied in metastasis prediction, drug response prediction, and prognosis of melanoma. Besides, patients' perspectives of artificial intelligence and collaboration of human and artificial intelligence in melanoma also attracted attention. The query term might not include all articles, and we could not examine the algorithms that were built without publication. CONCLUSION The performance of artificial intelligence in melanoma is satisfactory and the future for potential applications is enormous.
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Affiliation(s)
- Shu Zhang
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Yuanzhuo Wang
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Qingyue Zheng
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jiarui Li
- Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jiuzuo Huang
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xiao Long
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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Lboukili I, Stamatas G, Descombes X. Automating reflectance confocal microscopy image analysis for dermatological research: a review. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220021VRR. [PMID: 35879817 PMCID: PMC9309100 DOI: 10.1117/1.jbo.27.7.070902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 07/08/2022] [Indexed: 05/31/2023]
Abstract
SIGNIFICANCE Reflectance confocal microscopy (RCM) is a noninvasive, in vivo technology that offers near histopathological resolution at the cellular level. It is useful in the study of phenomena for which obtaining a biopsy is impractical or would cause unnecessary tissue damage and trauma to the patient. AIM This review covers the use of RCM in the study of skin and the use of machine learning to automate information extraction. It has two goals: (1) an overview of information provided by RCM on skin structure and how it changes over time in response to stimuli and in disease and (2) an overview of machine learning approaches developed to automate the extraction of key morphological features from RCM images. APPROACH A PubMed search was conducted with additional literature obtained from references lists. RESULTS The application of RCM as an in vivo tool in dermatological research and the biologically relevant information derived from it are presented. Algorithms for image classification to epidermal layers, delineation of the dermal-epidermal junction, classification of skin lesions, and demarcation of individual cells within an image, all important factors in the makeup of the skin barrier, were reviewed. Application of image analysis methods in RCM is hindered by low image quality due to noise and/or poor contrast. Use of supervised machine learning is limited by time-consuming manual labeling of RCM images. CONCLUSIONS RCM has great potential in the study of skin structures. The use of artificial intelligence could enable an easier, more reproducible, precise, and rigorous study of RCM images for the understanding of skin structures, skin barrier, and skin inflammation and lesions. Although several attempts have been made, further work is still needed to provide a definite gold standard and overcome issues related to image quality, limited labeled datasets, and lack of phenotype variability in available databases.
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Chen M, Feng X, Fox MC, Reichenberg JS, Lopes FCPS, Sebastian KR, Markey MK, Tunnell JW. Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:065004. [PMID: 35773774 PMCID: PMC9243521 DOI: 10.1117/1.jbo.27.6.065004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/09/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues structures. Reflectance confocal microscopy (RCM) provides morphological and cytological details by which many features of epidermis and hair follicles can be readily identified. Combining RS with deep-learning-aided RCM has the potential to improve the diagnostic accuracy of RS in an automated fashion, without requiring additional input from the clinician. AIM The aim of this study is to improve the specificity of RS for detecting basal cell carcinoma (BCC) using an artificial neural network trained on RCM images to identify false positive normal skin structures (hair follicles and epidermis). APPROACH Our approach was to build a two-step classification model. In the first step, a Raman biophysical model that was used in prior work classified BCC tumors from normal tissue structures with high sensitivity. In the second step, 191 RCM images were collected from the same site as the Raman data and served as inputs for two ResNet50 networks. The networks selected the hair structure and epidermis images, respectively, within all images corresponding to the positive predictions of the Raman biophysical model with high specificity. The specificity of the BCC biophysical model was improved by moving the Raman spectra corresponding to these selected images from false positive to true negative. RESULTS Deep-learning trained on RCM images removed 52% of false positive predictions from the Raman biophysical model result while maintaining a sensitivity of 100%. The specificity was improved from 84.2% using Raman spectra alone to 92.4% by integrating Raman spectra with RCM images. CONCLUSIONS Combining RS with deep-learning-aided RCM imaging is a promising tool for guiding tumor resection surgery.
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Affiliation(s)
- Mengkun Chen
- The University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
| | - Xu Feng
- The University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
| | - Matthew C. Fox
- The University of Texas at Austin, Division of Dermatology, Dell Medical School, Austin, Texas, United States
| | - Jason S. Reichenberg
- The University of Texas at Austin, Division of Dermatology, Dell Medical School, Austin, Texas, United States
| | - Fabiana C. P. S. Lopes
- The University of Texas at Austin, Division of Dermatology, Dell Medical School, Austin, Texas, United States
| | - Katherine R. Sebastian
- The University of Texas at Austin, Division of Dermatology, Dell Medical School, Austin, Texas, United States
| | - Mia K. Markey
- The University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
- The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, Texas, United States
| | - James W. Tunnell
- The University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
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Malciu AM, Lupu M, Voiculescu VM. Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology. J Clin Med 2022; 11:jcm11020429. [PMID: 35054123 PMCID: PMC8780225 DOI: 10.3390/jcm11020429] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 12/22/2022] Open
Abstract
Reflectance confocal microscopy (RCM) is a non-invasive imaging method designed to identify various skin diseases. Confocal based diagnosis may be subjective due to the learning curve of the method, the scarcity of training programs available for RCM, and the lack of clearly defined diagnostic criteria for all skin conditions. Given that in vivo RCM is becoming more widely used in dermatology, numerous deep learning technologies have been developed in recent years to provide a more objective approach to RCM image analysis. Machine learning-based algorithms are used in RCM image quality assessment to reduce the number of artifacts the operator has to view, shorten evaluation times, and decrease the number of patient visits to the clinic. However, the current visual method for identifying the dermal-epidermal junction (DEJ) in RCM images is subjective, and there is a lot of variation. The delineation of DEJ on RCM images could be automated through artificial intelligence, saving time and assisting novice RCM users in studying the key DEJ morphological structure. The purpose of this paper is to supply a current summary of machine learning and artificial intelligence’s impact on the quality control of RCM images, key morphological structures identification, and detection of different skin lesion types on static RCM images.
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Affiliation(s)
- Ana Maria Malciu
- Department of Dermatology, Elias University Emergency Hospital, 011461 Bucharest, Romania;
| | - Mihai Lupu
- Department of Dermatology, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Correspondence: (M.L.); (V.M.V.)
| | - Vlad Mihai Voiculescu
- Department of Dermatology, Elias University Emergency Hospital, 011461 Bucharest, Romania;
- Department of Dermatology, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Correspondence: (M.L.); (V.M.V.)
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Progress in the application of reflectance confocal microscopy in dermatology. Postepy Dermatol Alergol 2021; 38:709-715. [PMID: 34849113 PMCID: PMC8610039 DOI: 10.5114/ada.2021.110077] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Accepted: 12/10/2019] [Indexed: 12/23/2022] Open
Abstract
Reflectance confocal microscopy (RCM) is abbreviated as skin three-dimensional computed tomography, which can help clearly observe the structure of the epidermis and superficial dermis. It is a non-invasive skin disease examination method and provides fast access to real-time, dynamic skin micro-anatomical images. Therefore, RCM is widely used in the clinical diagnosis of skin diseases. For example, the RCM features of vitiligo are as follows: pigment loss or partial pigment loss in the lesion area, loss of the basal layer pigment ring. The RCM findings of Riehl melanosis are as follows: basal cell liquefaction and degeneration. The RCM results for verruca plana show: the Rose-like structure. The characteristics of psoriasis under RCM include: hyperkeratosis, parakeratosis, thickening of the spinous layer, capillary dilatation and hyperaemia, peripheral inflammatory cell infiltration. Epidermal brain-like structure was observed under RCM of seborrheic keratosis. With RCM, image acquisition and preservation of the skin is convenient, and the technique is convenient for comparing the development of lesions during long-term follow-up observation. Therefore, it helps to understand disease development in real time and dynamically and can be used to evaluate the curative effect. In this article, we briefly review the technical principles, diagnostic criteria for RCM application and RCM-related research progress in the diagnosis of pigmentary diseases, inflammatory diseases, skin tumours, and other common skin diseases.
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Abstract
Dermatology and medicine are producing data at an increasing rate that are progressively difficult to sort and manage. Artificial intelligence (AI) and machine learning are examples of tools that may have the capability to produce significant and meaningful results from these data. Currently, AI and machine learning have a variety of applications in medicine including, but not limited to, diagnostics, patient management, preventive medicine, and genomic analysis. Although the role of AI in dermatology is greater than ever, its use is still extremely limited. As AI is continually developed and implemented, it is essential that stakeholders understand AI terminology, applications, limitations, and projected uses in dermatology. With the continued development of AI technology, however, its implementation may afford greater dermatologist efficiency, greater increased patient access to dermatologic care, and improved patient outcomes.
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Affiliation(s)
- Chandler W Rundle
- Department of Dermatology, University of Colorado School of Medicine, Denver, Colorado, USA
| | - Parker Hollingsworth
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Robert P Dellavalle
- Department of Dermatology, University of Colorado School of Medicine, Denver, Colorado, USA; Department of Public Health, University of Colorado School of Medicine, Denver, Colorado, USA; Dermatology Service, US Department of Veterans Affairs, Eastern Colorado Health Care System, Aurora, Colorado, USA.
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Hanlon KL, Wei G, Braue J, Correa-Selm L, Grichnik JM. Improving dermal level images from reflectance confocal microscopy using wavelet-based transformations and adaptive histogram equalization. Lasers Surg Med 2021; 54:384-391. [PMID: 34633691 DOI: 10.1002/lsm.23483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/10/2021] [Accepted: 09/25/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVES Reflectance confocal microscopy (RCM) generates scalar image data from serial depths in the skin, allowing in vivo examination of cellular features. The maximum imaging depth of RCM is approximately 250 µm, to the papillary dermis, or upper reticular dermis. Frequently, important diagnostic features are present in the dermis, hence improved visualization of deeper levels is advantageous. METHODS Low contrast and noise in dermal images were improved by employing a combination of wavelet-based transformations and contrast-limited adaptive histogram equalization. RESULTS Preserved details, noise reduction, increased contrast, and feature enhancement were observed in the resulting processed images. CONCLUSIONS Complex and combined wavelet-based enhancement approaches for dermal level images yielded reconstructions of higher quality than less sophisticated histogram-based strategies. Image optimization may improve the diagnostic accuracy of RCM, especially for entities with dermal findings.
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Affiliation(s)
- Katharine L Hanlon
- Department of Cutaneous Oncology, Cleveland Clinic Indian River Hospital, Scully Welsh Cancer Center, Vero Beach, Florida, USA.,Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Grace Wei
- Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Jonathan Braue
- Department of Cutaneous Oncology, Cleveland Clinic Indian River Hospital, Scully Welsh Cancer Center, Vero Beach, Florida, USA
| | - Lilia Correa-Selm
- Department of Cutaneous Oncology, Cleveland Clinic Indian River Hospital, Scully Welsh Cancer Center, Vero Beach, Florida, USA.,Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - James M Grichnik
- Department of Cutaneous Oncology, Cleveland Clinic Indian River Hospital, Scully Welsh Cancer Center, Vero Beach, Florida, USA.,Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
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Sikorska M, Skalski A, Wodzinski M, Witkowski A, Pellacani G, Ludzik J. Learning-based local quality assessment of reflectance confocal microscopy images for dermatology applications. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Meng X, Chen J, Zhang Z, Li K, Li J, Yu Z, Zhang Y. Non-invasive optical methods for melanoma diagnosis. Photodiagnosis Photodyn Ther 2021; 34:102266. [PMID: 33785441 DOI: 10.1016/j.pdpdt.2021.102266] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/09/2021] [Accepted: 03/22/2021] [Indexed: 02/07/2023]
Abstract
Cutaneous melanoma is one of the most common malignancies with increased incidence in the past few decades, making it a significant public health problem. The early diagnosis of melanoma is a major factor in improving patient's survival. The traditional pathway to melanoma diagnosis starts with a visual diagnosis, followed by subsequent biopsy and histopathologic evaluation. Recently, multiple innovative optical technology-based methods, including dermoscopy, reflectance confocal microscopy, optical coherence tomography, multiphoton excited fluorescence imaging and stepwise two-photon excited fluorescence (dermatofluoroscopy), have been developed to increase the diagnostic accuracy for the non-invasive melanoma diagnosis. Some of them have already been applied to real-life clinical settings, others require more research and development. These technologies show promise in facilitating the diagnosis of melanoma since they are non-invasive, sensitive, objective and easy to apply. Diagnostic accuracy, detection time, portability and the cost-effectiveness of the device are all aspects that need to be improved. This article reviews the method of these emerging optical non-invasive diagnostic technologies, their clinical application, their benefits and limitations, as well as their possible future development.
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Affiliation(s)
- Xinxian Meng
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Jun Chen
- Department of Dermatology and Dermatologic Surgery, Shanghai Ninth People's Hospital, Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Zheng Zhang
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Ke Li
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Jie Li
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Zhixi Yu
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Yixin Zhang
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, China; Department of Laser and Aesthetic Medicine, Shanghai Ninth People's Hospital, Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, China.
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15
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Mehrabi JN, Baugh EG, Fast A, Lentsch G, Balu M, Lee BA, Kelly KM. A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology. Lasers Surg Med 2021; 53:1011-1019. [PMID: 33476062 DOI: 10.1002/lsm.23376] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/30/2020] [Accepted: 12/25/2020] [Indexed: 01/03/2023]
Abstract
BACKGROUND AND OBJECTIVES Non-invasive optical imaging has the potential to provide a diagnosis without the need for biopsy. One such technology is reflectance confocal microscopy (RCM), which uses low power, near-infrared laser light to enable real-time in vivo visualization of superficial human skin from the epidermis down to the papillary dermis. Although RCM has great potential as a diagnostic tool, there is a need for the development of reliable image analysis programs, as acquired grayscale images can be difficult and time-consuming to visually assess. The purpose of this review is to provide a clinical perspective on the current state of artificial intelligence (AI) for the analysis and diagnostic utility of RCM imaging. STUDY DESIGN/MATERIALS AND METHODS A systematic PubMed search was conducted with additional relevant literature obtained from reference lists. RESULTS Algorithms used for skin stratification, classification of pigmented lesions, and the quantification of photoaging were reviewed. Image segmentation, statistical methods, and machine learning techniques are among the most common methods used to analyze RCM image stacks. The poor visual contrast within RCM images and difficulty navigating image stacks were mediated by machine learning algorithms, which allowed the identification of specific skin layers. CONCLUSIONS AI analysis of RCM images has the potential to increase the clinical utility of this emerging technology. A number of different techniques have been utilized but further refinements are necessary to allow consistent accurate assessments for diagnosis. The automated detection of skin cancers requires more development, but future applications are truly boundless, and it is compelling to envision the role that AI will have in the practice of dermatology. Lasers Surg. Med. © 2020 Wiley Periodicals LLC.
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Affiliation(s)
- Joseph N Mehrabi
- Department of Dermatology, University of California, Irvine, California, 92697
| | - Erica G Baugh
- Department of Dermatology, University of California, Irvine, California, 92697
| | - Alexander Fast
- Beckman Laser Institute, University of California Irvine, Irvine, California, 92612
| | - Griffin Lentsch
- Beckman Laser Institute, University of California Irvine, Irvine, California, 92612
| | - Mihaela Balu
- Beckman Laser Institute, University of California Irvine, Irvine, California, 92612
| | - Bonnie A Lee
- Department of Dermatology, University of California, Irvine, California, 92697
| | - Kristen M Kelly
- Department of Dermatology, University of California, Irvine, California, 92697.,Beckman Laser Institute, University of California Irvine, Irvine, California, 92612
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16
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Gong C, Stratton DB, Curiel-Lewandrowski CN, Kang D. Speckle-free, near-infrared portable confocal microscope. APPLIED OPTICS 2020; 59:G41-G46. [PMID: 32749315 PMCID: PMC8273882 DOI: 10.1364/ao.392004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 04/23/2020] [Indexed: 05/23/2023]
Abstract
We have developed a portable confocal microscope (PCM) that uses an inexpensive near-infrared LED as the light source. Use of the spatially incoherent light source significantly reduced the speckle contrast. The PCM device was manufactured at the material cost of approximately $5000 and weighed only 1 kg. Lateral and axial resolutions were measured as 1.6 and 6.0 µm, respectively. Preliminary in vivo skin imaging experiment results showed that the PCM device could visualize characteristic cellular features of human skin extending from the stratum corneum to the superficial dermis. Dynamic imaging of blood flow in vivo was also demonstrated. The capability to visualize cellular features up to the superficial dermis is expected to facilitate evaluation and clinical adoption of this low-cost diagnostic imaging tool.
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Affiliation(s)
- Cheng Gong
- Wyant College of Optical Sciences, The University of Arizona, 1630 E University Blvd, Tucson, AZ 85719, USA
| | - Delaney B. Stratton
- Banner - University Medicine Dermatology Clinic, 7165 N Pima Canyon Dr, Tucson, AZ 85718, USA
| | - Clara N. Curiel-Lewandrowski
- Banner - University Medicine Dermatology Clinic, 7165 N Pima Canyon Dr, Tucson, AZ 85718, USA
- University of Arizona Cancer Center, 3838 N Campbell Ave, Tucson, AZ 85719, USA
| | - Dongkyun Kang
- Wyant College of Optical Sciences, The University of Arizona, 1630 E University Blvd, Tucson, AZ 85719, USA
- University of Arizona Cancer Center, 3838 N Campbell Ave, Tucson, AZ 85719, USA
- Department of Biomedical Engineering, The University of Arizona, 1657 E Helen St, Tucson, AZ 85719, USA
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17
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Coakley A, Orlowski TJ, Muhlbauer A, Moy L, Speiser JJ. A comparison of imaging software and conventional cell counting in determining melanocyte density in photodamaged control sample and melanoma in situ biopsies. J Cutan Pathol 2020; 47:675-680. [PMID: 32159867 DOI: 10.1111/cup.13681] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 02/11/2020] [Accepted: 02/23/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND Objective methods for distinguishing melanoma in situ (MIS) from photodamaged skin (PS) are needed to guide treatment in patients with melanocytic proliferations. Melanocyte density (MD) could serve as an objective histopathological criterion in difficult cases. Calculating MD via manual cell counts (MCC) with immunohistochemical (IHC)-stained slides has been previously published. However, the clinical application of this method is questionable, as quantification of MD via MCC on difficult cases is time consuming, especially in high volume practices. METHODS ImageJ is an image processing software that uses scanned slide images to determine cell count. In this study, we compared MCC to ImageJ calculated MD in microphthalmia transcription factor-IHC stained MIS biopsies and control PS acquired from the same patients. RESULTS We found a statistically significant difference in MD between PS and MIS as measured by both MCC and ImageJ software (P < 0.01). Additionally, no statistically significant difference was found when comparing MD measurements recorded by ImageJ vs those determined by the MCC method. CONCLUSION MD as determined by ImageJ strongly correlates with the MD calculated by MCC. We propose the use of ImageJ as a time-efficient, objective, and reproducible tool to assess MD.
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Affiliation(s)
- Anne Coakley
- Division of Dermatopathology, Department of Pathology, Loyola University Medical Center, Maywood, Illinois, USA, USA
| | - Timothy J Orlowski
- 479th Flying Training Group, Aviation Medicine Department, Naval Hospital Pensacola, Pensacola, Florida, USA, USA
| | - Aaron Muhlbauer
- Division of Dermatopathology, Department of Pathology, Loyola University Medical Center, Maywood, Illinois, USA, USA
| | - Lauren Moy
- Section of Dermatology, Department of Internal Medicine, Loyola University Medical Center, Maywood, Illinois, USA, USA
| | - Jodi J Speiser
- Division of Dermatopathology, Department of Pathology, Loyola University Medical Center, Maywood, Illinois, USA, USA
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Wodzinski M, Pajak M, Skalski A, Witkowski A, Pellacani G, Ludzik J. Automatic Quality Assessment of Reflectance Confocal Microscopy Mosaics using Attention-Based Deep Neural Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1824-1827. [PMID: 33018354 DOI: 10.1109/embc44109.2020.9176557] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Skin cancers are the most common cancers with an increased incidence, and a valid, early diagnosis may significantly reduce its morbidity and mortality. Reflectance confocal microscopy (RCM) is a relatively new, non-invasive imaging technique that allows screening lesions at a cellular resolution. However, one of the main disadvantages of the RCM is frequently occurring artifacts which makes the diagnostic process more time consuming and hard to automate using e.g. end-to-end deep learning approach. A tool to automatically determine the RCM mosaic quality could be beneficial for both the lesion classification and informing the user (dermatologist) about its quality in real-time, during the examination procedure. In this work, we propose an attention-based deep network to automatically determine if a given RCM mosaic has an acceptable quality. We achieved accuracy above 87% on the test set which may considerably improve further classification results and the RCM-based examination.
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19
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Kose K, Bozkurt A, Alessi-Fox C, Brooks DH, Dy JG, Rajadhyaksha M, Gill M. Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy. J Invest Dermatol 2020; 140:1214-1222. [PMID: 31838127 PMCID: PMC7967900 DOI: 10.1016/j.jid.2019.10.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 10/09/2019] [Accepted: 10/16/2019] [Indexed: 10/25/2022]
Abstract
In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal layers while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is expanding from real-time diagnosis at the bedside to include a capture, store, and forward model with image interpretation and diagnosis occurring offsite, similar to radiology. As the patient may no longer be present at the time of image interpretation, quality assurance is key during image acquisition. Herein, we introduce a quality assurance process by means of automatically quantifying diagnostically uninformative areas within the lesional area by using RCM and coregistered dermoscopy images together. We trained and validated a pixel-level segmentation model on 117 RCM mosaics collected by international collaborators. The model delineates diagnostically uninformative areas with 82% sensitivity and 93% specificity. We further tested the model on a separate set of 372 coregistered RCM-dermoscopic image pairs and illustrate how the results of the RCM-only model can be improved via a multimodal (RCM + dermoscopy) approach, which can help quantify the uninformative regions within the lesional area. Our data suggest that machine learning-based automatic quantification offers a feasible objective quality control measure for RCM imaging.
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Affiliation(s)
- Kivanc Kose
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
| | - Alican Bozkurt
- Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, USA
| | | | - Dana H Brooks
- Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, USA
| | - Jennifer G Dy
- Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, USA
| | - Milind Rajadhyaksha
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Melissa Gill
- Department of Pathology, SUNY Downstate Medical Center, Brooklyn, New York, USA; SkinMedical Research and Diagnostics, PLLC, Dobbs Ferry, New York, USA
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20
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Wodzinski M, Skalski A, Witkowski A, Pellacani G, Ludzik J. Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:4754-4757. [PMID: 31946924 DOI: 10.1109/embc.2019.8856731] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both morbidity and mortality. RCM is an in-vivo non-invasive screening tool that produces virtual biopsies of skin lesions but its proficient and safe use requires hard to obtain expertise. Therefore, it may be useful to have an additional tool to aid diagnosis. The proposed network is based on the ResNet architecture. The dataset consists of 429 RCM mosaics and is divided into 3 classes: melanoma, basal cell carcinoma, and benign naevi with the ground-truth confirmed by a histopathological examination. The test set classification accuracy was 87%, higher than the accuracy achieved by medical, confocal users. The results show that the proposed classification system can be a useful tool to aid in early, noninvasive melanoma detection.
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21
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Magalhaes C, Mendes J, Vardasca R. The role of AI classifiers in skin cancer images. Skin Res Technol 2019; 25:750-757. [DOI: 10.1111/srt.12713] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 04/28/2019] [Indexed: 01/29/2023]
Affiliation(s)
- Carolina Magalhaes
- INEGI‐LAETA Faculdade de Engenharia Universidade do Porto Porto Portugal
| | - Joaquim Mendes
- INEGI‐LAETA Faculdade de Engenharia Universidade do Porto Porto Portugal
| | - Ricardo Vardasca
- INEGI‐LAETA Faculdade de Engenharia Universidade do Porto Porto Portugal
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22
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Kromenacker B, Maarouf M, Shi VY. Augmented Intelligence in Dermatology: Fantasy or Future? Dermatology 2019; 235:250-252. [PMID: 30889594 DOI: 10.1159/000497275] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 01/25/2019] [Indexed: 11/19/2022] Open
Affiliation(s)
| | - Melody Maarouf
- College of Medicine, University of Arizona, Tucson, Arizona, USA
| | - Vivian Yan Shi
- Division of Dermatology, Department of Medicine, University of Arizona, Tucson, Arizona, USA,
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23
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Dinnes J, Deeks JJ, Saleh D, Chuchu N, Bayliss SE, Patel L, Davenport C, Takwoingi Y, Godfrey K, Matin RN, Patalay R, Williams HC, Cochrane Skin Cancer Diagnostic Test Accuracy Group, Cochrane Skin Group. Reflectance confocal microscopy for diagnosing cutaneous melanoma in adults. Cochrane Database Syst Rev 2018; 12:CD013190. [PMID: 30521681 PMCID: PMC6492459 DOI: 10.1002/14651858.cd013190] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Melanoma has one of the fastest rising incidence rates of any cancer. It accounts for a small percentage of skin cancer cases but is responsible for the majority of skin cancer deaths. Early detection and treatment is key to improving survival; however, anxiety around missing early cases needs to be balanced against appropriate levels of referral and excision of benign lesions. Used in conjunction with clinical or dermoscopic suspicion of malignancy, or both, reflectance confocal microscopy (RCM) may reduce unnecessary excisions without missing melanoma cases. OBJECTIVES To determine the diagnostic accuracy of reflectance confocal microscopy for the detection of cutaneous invasive melanoma and atypical intraepidermal melanocytic variants in adults with any lesion suspicious for melanoma and lesions that are difficult to diagnose, and to compare its accuracy with that of dermoscopy. SEARCH METHODS We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials; MEDLINE; Embase; and seven other databases. We studied reference lists and published systematic review articles. SELECTION CRITERIA Studies of any design that evaluated RCM alone, or RCM in comparison to dermoscopy, in adults with lesions suspicious for melanoma or atypical intraepidermal melanocytic variants, compared with a reference standard of either histological confirmation or clinical follow-up. DATA COLLECTION AND ANALYSIS Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). We contacted authors of included studies where information related to the target condition or diagnostic threshold were missing. We estimated summary sensitivities and specificities per algorithm and threshold using the bivariate hierarchical model. To compare RCM with dermoscopy, we grouped studies by population (defined by difficulty of lesion diagnosis) and combined data using hierarchical summary receiver operating characteristic (SROC) methods. Analysis of studies allowing direct comparison between tests was undertaken. To facilitate interpretation of results, we computed values of specificity at the point on the SROC curve with 90% sensitivity as this value lies within the estimates for the majority of analyses. We investigated the impact of using a purposely developed RCM algorithm and in-person test interpretation. MAIN RESULTS The search identified 18 publications reporting on 19 study cohorts with 2838 lesions (including 658 with melanoma), which provided 67 datasets for RCM and seven for dermoscopy. Studies were generally at high or unclear risk of bias across almost all domains and of high or unclear concern regarding applicability of the evidence. Selective participant recruitment, lack of blinding of the reference test to the RCM result, and differential verification were particularly problematic. Studies may not be representative of populations eligible for RCM, and test interpretation was often undertaken remotely from the patient and blinded to clinical information.Meta-analysis found RCM to be more accurate than dermoscopy in studies of participants with any lesion suspicious for melanoma and in participants with lesions that were more difficult to diagnose (equivocal lesion populations). Assuming a fixed sensitivity of 90% for both tests, specificities were 82% for RCM and 42% for dermoscopy for any lesion suspicious for melanoma (9 RCM datasets; 1452 lesions and 370 melanomas). For a hypothetical population of 1000 lesions at the median observed melanoma prevalence of 30%, this equated to a reduction in unnecessary excisions with RCM of 280 compared to dermoscopy, with 30 melanomas missed by both tests. For studies in equivocal lesions, specificities of 86% would be observed for RCM and 49% for dermoscopy (7 RCM datasets; 1177 lesions and 180 melanomas). At the median observed melanoma prevalence of 20%, this reduced unnecessary excisions by 296 with RCM compared with dermoscopy, with 20 melanomas missed by both tests. Across all populations, algorithms and thresholds assessed, the sensitivity and specificity of the Pellacani RCM score at a threshold of three or greater were estimated at 92% (95% confidence interval (CI) 87 to 95) for RCM and 72% (95% CI 62 to 81) for dermoscopy. AUTHORS' CONCLUSIONS RCM may have a potential role in clinical practice, particularly for the assessment of lesions that are difficult to diagnose using visual inspection and dermoscopy alone, where the evidence suggests that RCM may be both more sensitive and specific in comparison to dermoscopy. Given the paucity of data to allow comparison with dermoscopy, the results presented require further confirmation in prospective studies comparing RCM with dermoscopy in a real-world setting in a representative population.
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Affiliation(s)
- Jacqueline Dinnes
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Jonathan J Deeks
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Daniel Saleh
- Newcastle Hospitals NHS Trust, Royal Victoria InfirmaryNewcastle HospitalsNewcastleUK
- The University of Queensland, PA‐Southside Clinical UnitSchool of Clinical MedicineBrisbaneQueenslandAustralia
| | - Naomi Chuchu
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
| | - Susan E Bayliss
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
| | - Lopa Patel
- Royal Stoke HospitalPlastic SurgeryStoke‐on‐TrentStaffordshireUKST4 6QG
| | - Clare Davenport
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
| | - Yemisi Takwoingi
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Kathie Godfrey
- The University of Nottinghamc/o Cochrane Skin GroupNottinghamUK
| | - Rubeta N Matin
- Churchill HospitalDepartment of DermatologyOld RoadHeadingtonOxfordUKOX3 7LE
| | - Rakesh Patalay
- Guy's and St Thomas' NHS Foundation TrustDepartment of DermatologyDSLU, Cancer CentreGreat Maze PondLondonUKSE1 9RT
| | - Hywel C Williams
- University of NottinghamCentre of Evidence Based DermatologyQueen's Medical CentreDerby RoadNottinghamUKNG7 2UH
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Dinnes J, Deeks JJ, Chuchu N, Saleh D, Bayliss SE, Takwoingi Y, Davenport C, Patel L, Matin RN, O'Sullivan C, Patalay R, Williams HC, Cochrane Skin Cancer Diagnostic Test Accuracy Group, Cochrane Skin Group. Reflectance confocal microscopy for diagnosing keratinocyte skin cancers in adults. Cochrane Database Syst Rev 2018; 12:CD013191. [PMID: 30521687 PMCID: PMC6516892 DOI: 10.1002/14651858.cd013191] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND Early accurate detection of all skin cancer types is important to guide appropriate management and improve morbidity and survival. Basal cell carcinoma (BCC) is usually a localised skin cancer but with potential to infiltrate and damage surrounding tissue, whereas cutaneous squamous cell carcinoma (cSCC) and melanoma are higher risk skin cancers with the potential to metastasise and ultimately lead to death. When used in conjunction with clinical or dermoscopic suspicion of malignancy, or both, reflectance confocal microscopy (RCM) may help to identify cancers eligible for non-surgical treatment without the need for a diagnostic biopsy, particularly in people with suspected BCC. Any potential benefit must be balanced against the risk of any misdiagnoses. OBJECTIVES To determine the diagnostic accuracy of RCM for the detection of BCC, cSCC, or any skin cancer in adults with any suspicious lesion and lesions that are difficult to diagnose (equivocal); and to compare its accuracy with that of usual practice (visual inspection or dermoscopy, or both). SEARCH METHODS We undertook a comprehensive search of the following databases from inception to August 2016: Cochrane Central Register of Controlled Trials; MEDLINE; Embase; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists and published systematic review articles. SELECTION CRITERIA Studies of any design that evaluated the accuracy of RCM alone, or RCM in comparison to visual inspection or dermoscopy, or both, in adults with lesions suspicious for skin cancer compared with a reference standard of either histological confirmation or clinical follow-up, or both. DATA COLLECTION AND ANALYSIS Two review authors independently extracted data using a standardised data extraction and quality assessment form (based on QUADAS-2). We contacted authors of included studies where information related to the target condition or diagnostic threshold were missing. We estimated summary sensitivities and specificities using the bivariate hierarchical model. For computation of likely numbers of true-positive, false-positive, false-negative, and true-negative findings in the 'Summary of findings' tables, we applied summary sensitivity and specificity estimates to lower quartile, median and upper quartiles of the prevalence observed in the study groups. We also investigated the impact of observer experience. MAIN RESULTS The review included 10 studies reporting on 11 study cohorts. All 11 cohorts reported data for the detection of BCC, including 2037 lesions (464 with BCC); and four cohorts reported data for the detection of cSCC, including 834 lesions (71 with cSCC). Only one study also reported data for the detection of BCC or cSCC using dermoscopy, limiting comparisons between RCM and dermoscopy. Studies were at high or unclear risk of bias across almost all methodological quality domains, and were of high or unclear concern regarding applicability of the evidence. Selective participant recruitment, unclear blinding of the reference test, and exclusions due to image quality or technical difficulties were observed. It was unclear whether studies were representative of populations eligible for testing with RCM, and test interpretation was often undertaken using images, remotely from the participant and the interpreter blinded to clinical information that would normally be available in practice.Meta-analysis found RCM to be more sensitive but less specific for the detection of BCC in studies of participants with equivocal lesions (sensitivity 94%, 95% confidence interval (CI) 79% to 98%; specificity 85%, 95% CI 72% to 92%; 3 studies) compared to studies that included any suspicious lesion (sensitivity 76%, 95% CI 45% to 92%; specificity 95%, 95% CI 66% to 99%; 4 studies), although CIs were wide. At the median prevalence of disease of 12.5% observed in studies including any suspicious lesion, applying these results to a hypothetical population of 1000 lesions results in 30 BCCs missed with 44 false-positive results (lesions misdiagnosed as BCCs). At the median prevalence of disease of 15% observed in studies of equivocal lesions, nine BCCs would be missed with 128 false-positive results in a population of 1000 lesions. Across both sets of studies, up to 15% of these false-positive lesions were observed to be melanomas mistaken for BCCs. There was some suggestion of higher sensitivities in studies with more experienced observers. Summary sensitivity and specificity could not be estimated for the detection of cSCC due to paucity of data. AUTHORS' CONCLUSIONS There is insufficient evidence for the use of RCM for the diagnosis of BCC or cSCC in either population group. A possible role for RCM in clinical practice is as a tool to avoid diagnostic biopsies in lesions with a relatively high clinical suspicion of BCC. The potential for, and consequences of, misclassification of other skin cancers such as melanoma as BCCs requires further research. Importantly, data are lacking that compare RCM to standard clinical practice (with or without dermoscopy).
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Affiliation(s)
- Jacqueline Dinnes
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Jonathan J Deeks
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Naomi Chuchu
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
| | - Daniel Saleh
- Newcastle Hospitals NHS Trust, Royal Victoria InfirmaryNewcastle HospitalsNewcastleUK
- The University of Queensland, PA‐Southside Clinical UnitSchool of Clinical MedicineBrisbaneQueenslandAustralia
| | - Susan E Bayliss
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
| | - Yemisi Takwoingi
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Clare Davenport
- University of BirminghamInstitute of Applied Health ResearchBirminghamUKB15 2TT
| | - Lopa Patel
- Royal Stoke HospitalPlastic SurgeryStoke‐on‐TrentStaffordshireUKST4 6QG
| | - Rubeta N Matin
- Churchill HospitalDepartment of DermatologyOld RoadHeadingtonOxfordUKOX3 7LE
| | | | - Rakesh Patalay
- Guy's and St Thomas' NHS Foundation TrustDepartment of DermatologyDSLU, Cancer CentreGreat Maze PondLondonUKSE1 9RT
| | - Hywel C Williams
- University of NottinghamCentre of Evidence Based DermatologyQueen's Medical CentreDerby RoadNottinghamUKNG7 2UH
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Halimi A, Batatia H, Le Digabel J, Josse G, Tourneret JY. Wavelet-based statistical classification of skin images acquired with reflectance confocal microscopy. BIOMEDICAL OPTICS EXPRESS 2017; 8:5450-5467. [PMID: 29296480 PMCID: PMC5745095 DOI: 10.1364/boe.8.005450] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 10/18/2017] [Accepted: 10/19/2017] [Indexed: 06/07/2023]
Abstract
Detecting skin lentigo in reflectance confocal microscopy images is an important and challenging problem. This imaging modality has not yet been widely investigated for this problem and there are a few automatic processing techniques. They are mostly based on machine learning approaches and rely on numerous classical image features that lead to high computational costs given the very large resolution of these images. This paper presents a detection method with very low computational complexity that is able to identify the skin depth at which the lentigo can be detected. The proposed method performs multiresolution decomposition of the image obtained at each skin depth. The distribution of image pixels at a given depth can be approximated accurately by a generalized Gaussian distribution whose parameters depend on the decomposition scale, resulting in a very-low-dimension parameter space. SVM classifiers are then investigated to classify the scale parameter of this distribution allowing real-time detection of lentigo. The method is applied to 45 healthy and lentigo patients from a clinical study, where sensitivity of 81.4% and specificity of 83.3% are achieved. Our results show that lentigo is identifiable at depths between 50μm and 60μm, corresponding to the average location of the the dermoepidermal junction. This result is in agreement with the clinical practices that characterize the lentigo by assessing the disorganization of the dermoepidermal junction.
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Affiliation(s)
- Abdelghafour Halimi
- University of Toulouse, IRIT-INPT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7,
France
| | - Hadj Batatia
- University of Toulouse, IRIT-INPT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7,
France
| | - Jimmy Le Digabel
- Centre de Recherche sur la Peau, Pierre Fabre Dermo-Cosmétique, 2 rue Viguerie, 31025 Toulouse Cedex 3, France
| | - Gwendal Josse
- Centre de Recherche sur la Peau, Pierre Fabre Dermo-Cosmétique, 2 rue Viguerie, 31025 Toulouse Cedex 3, France
| | - Jean Yves Tourneret
- University of Toulouse, IRIT-INPT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7,
France
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Rajadhyaksha M, Marghoob A, Rossi A, Halpern AC, Nehal KS. Reflectance confocal microscopy of skin in vivo: From bench to bedside. Lasers Surg Med 2016; 49:7-19. [PMID: 27785781 DOI: 10.1002/lsm.22600] [Citation(s) in RCA: 146] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2016] [Indexed: 12/24/2022]
Abstract
Following more than two decades of effort, reflectance confocal microscopy (RCM) imaging of skin was granted codes for reimbursement by the US Centers for Medicare and Medicaid Services. Dermatologists in the USA have started billing and receiving reimbursement for the imaging procedure and for the reading and interpretation of images. RCM imaging combined with dermoscopic examination is guiding the triage of lesions into those that appear benign, which are being spared from biopsy, against those that appear suspicious, which are then biopsied. Thus far, a few thousand patients have been spared from biopsy of benign lesions. The journey of RCM imaging from bench to bedside is certainly a success story, but still much more work lies ahead toward wider dissemination, acceptance, and adoption. We present a brief review of RCM imaging and highlight key challenges and opportunities. The success of RCM imaging paves the way for other emerging optical technologies, as well-and our bet for the future is on multimodal approaches. Lasers Surg. Med. 49:7-19, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Milind Rajadhyaksha
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ashfaq Marghoob
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Anthony Rossi
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Allan C Halpern
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kishwer S Nehal
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York
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Bozkurt A, Kose K, Alessi-Fox C, Dy JG, Brooks DH, Rajadhyaksha M. Unsupervised delineation of stratum corneum using reflectance confocal microscopy and spectral clustering. Skin Res Technol 2016; 23:176-185. [PMID: 27516408 DOI: 10.1111/srt.12316] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2016] [Indexed: 11/30/2022]
Abstract
BACKGROUND Measuring the thickness of the stratum corneum (SC) in vivo is often required in pharmacological, dermatological, and cosmetological studies. Reflectance confocal microscopy (RCM) offers a non-invasive imaging-based approach. However, RCM-based measurements currently rely on purely visual analysis of images, which is time-consuming and suffers from inter-user subjectivity. METHODS We developed an unsupervised segmentation algorithm that can automatically delineate the SC layer in stacks of RCM images of human skin. We represent the unique textural appearance of SC layer using complex wavelet transform and distinguish it from deeper granular layers of skin using spectral clustering. Moreover, through localized processing in a matrix of small areas (called 'tiles'), we obtain lateral variation of SC thickness over the entire field of view. RESULTS On a set of 15 RCM stacks of normal human skin, our method estimated SC thickness with a mean error of 5.4 ± 5.1 μm compared to the 'ground truth' segmentation obtained from a clinical expert. CONCLUSION Our algorithm provides a non-invasive RCM imaging-based solution which is automated, rapid, objective, and repeatable.
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Affiliation(s)
- A Bozkurt
- Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA
| | - K Kose
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - C Alessi-Fox
- Caliber Imaging and Diagnostics, Rochester, NY, USA
| | - J G Dy
- Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA
| | - D H Brooks
- Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA
| | - M Rajadhyaksha
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Hames SC, Ardigò M, Soyer HP, Bradley AP, Prow TW. Automated Segmentation of Skin Strata in Reflectance Confocal Microscopy Depth Stacks. PLoS One 2016; 11:e0153208. [PMID: 27088865 PMCID: PMC4835045 DOI: 10.1371/journal.pone.0153208] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 03/25/2016] [Indexed: 11/24/2022] Open
Abstract
Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section. This work presents an automated approach using a bag of features approach to represent en-face sections and a logistic regression classifier to classify sections into one of four classes (stratum corneum, viable epidermis, dermal-epidermal junction and papillary dermis). This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20–30 and 50–70 years of age). The classification accuracy on the test set was 85.6%. The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm respectively. The probabilities predicted by the classifier in the test set showed that the classifier learned an effective model of the anatomy of human skin.
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Affiliation(s)
- Samuel C. Hames
- Dermatology Research Centre, The University of Queensland, School of Medicine, Translational Research Institute, Brisbane, Australia
| | - Marco Ardigò
- San Gallicano Dermatological Institute—IRCCS, Rome, Italy
| | - H. Peter Soyer
- Dermatology Research Centre, The University of Queensland, School of Medicine, Translational Research Institute, Brisbane, Australia
| | - Andrew P. Bradley
- The University of Queensland, School of Information Technology and Electrical Engineering, Brisbane, Australia
| | - Tarl W. Prow
- Dermatology Research Centre, The University of Queensland, School of Medicine, Translational Research Institute, Brisbane, Australia
- * E-mail:
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Leachman SA, Cassidy PB, Chen SC, Curiel C, Geller A, Gareau D, Pellacani G, Grichnik JM, Malvehy J, North J, Jacques SL, Petrie T, Puig S, Swetter SM, Tofte S, Weinstock MA. Methods of Melanoma Detection. Cancer Treat Res 2016; 167:51-105. [PMID: 26601859 DOI: 10.1007/978-3-319-22539-5_3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Detection and removal of melanoma, before it has metastasized, dramatically improves prognosis and survival. The purpose of this chapter is to (1) summarize current methods of melanoma detection and (2) review state-of-the-art detection methods and technologies that have the potential to reduce melanoma mortality. Current strategies for the detection of melanoma range from population-based educational campaigns and screening to the use of algorithm-driven imaging technologies and performance of assays that identify markers of transformation. This chapter will begin by describing state-of-the-art methods for educating and increasing awareness of at-risk individuals and for performing comprehensive screening examinations. Standard and advanced photographic methods designed to improve reliability and reproducibility of the clinical examination will also be reviewed. Devices that magnify and/or enhance malignant features of individual melanocytic lesions (and algorithms that are available to interpret the results obtained from these devices) will be compared and contrasted. In vivo confocal microscopy and other cellular-level in vivo technologies will be compared to traditional tissue biopsy, and the role of a noninvasive "optical biopsy" in the clinical setting will be discussed. Finally, cellular and molecular methods that have been applied to the diagnosis of melanoma, such as comparative genomic hybridization (CGH), fluorescent in situ hybridization (FISH), and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), will be discussed.
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Affiliation(s)
- Sancy A Leachman
- Department of Dermatology and Knight Cancer Institute, Oregon Health and Science University, 3303 SW Bond Avenue, CH16D, Portland, OR, 97239, USA.
| | - Pamela B Cassidy
- Department of Dermatology and Knight Cancer Institute, Oregon Health and Science University, 3125 SW Sam Jackson Park Road, L468R, Portland, OR, 97239, USA.
| | - Suephy C Chen
- Department of Dermatology, Emory University School of Medicine, 1525 Clifton Road NE, 1st Floor, Atlanta, GA, 30322, USA.
| | - Clara Curiel
- Department of Dermatology and Arizona Cancer Center, University of Arizona, 1515 N Campbell Avenue, Tucson, AZ, 85721, USA.
| | - Alan Geller
- Department of Dermatology, Harvard School of Public Health and Massachusetts General Hospital, Landmark Center, 401 Park Drive, 3rd Floor East, Boston, MA, 02215, USA.
| | - Daniel Gareau
- Laboratory of Investigative Dermatology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.
| | - Giovanni Pellacani
- Department of Dermatology, University of Modena and Reggio Emilia, Via del Pozzo 71, Modena, Italy.
| | - James M Grichnik
- Department of Dermatology and Cutaneous Surgery, University of Miami School of Medicine, Room 912, BRB (R-125), 1501 NW 10th Avenue, Miami, FL, 33136, USA.
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clinic Barcelona, Villarroel 170, 08036, Barcelona, Spain.
| | - Jeffrey North
- University of California, San Francisco, 1701 Divisadero Street, Suite 280, San Francisco, CA, 94115, USA.
| | - Steven L Jacques
- Department of Biomedical Engineering and Dermatology, Oregon Health and Science University, 3303 SW Bond Avenue, CH13B, Portland, OR, 97239, USA.
| | - Tracy Petrie
- Department of Biomedical Engineering, Oregon Health and Science University, 3303 SW Bond Avenue, CH13B, Portland, OR, 97239, USA.
| | - Susana Puig
- Melanoma Unit, Dermatology Department, Hospital Clinic Barcelona, Villarroel 170, 08036, Barcelona, Spain.
| | - Susan M Swetter
- Department of Dermatology/Cutaneous Oncology, Stanford University, 900 Blake Wilbur Drive, W3045, Stanford, CA, 94305, USA.
| | - Susan Tofte
- Department of Dermatology, Oregon Health and Science University, 3303 SW Bond Avenue, CH16D, Portland, OR, 97239, USA.
| | - Martin A Weinstock
- Departments of Dermatology and Epidemiology, Brown University, V A Medical Center 111D, 830 Chalkstone Avenue, Providence, RI, 02908, USA.
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Hames SC, Sinnya S, Tan JM, Morze C, Sahebian A, Soyer HP, Prow TW. Automated detection of actinic keratoses in clinical photographs. PLoS One 2015; 10:e0112447. [PMID: 25615930 PMCID: PMC4304708 DOI: 10.1371/journal.pone.0112447] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Accepted: 10/06/2014] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Clinical diagnosis of actinic keratosis is known to have intra- and inter-observer variability, and there is currently no non-invasive and objective measure to diagnose these lesions. OBJECTIVE The aim of this pilot study was to determine if automatically detecting and circumscribing actinic keratoses in clinical photographs is feasible. METHODS Photographs of the face and dorsal forearms were acquired in 20 volunteers from two groups: the first with at least on actinic keratosis present on the face and each arm, the second with no actinic keratoses. The photographs were automatically analysed using colour space transforms and morphological features to detect erythema. The automated output was compared with a senior consultant dermatologist's assessment of the photographs, including the intra-observer variability. Performance was assessed by the correlation between total lesions detected by automated method and dermatologist, and whether the individual lesions detected were in the same location as the dermatologist identified lesions. Additionally, the ability to limit false positives was assessed by automatic assessment of the photographs from the no actinic keratosis group in comparison to the high actinic keratosis group. RESULTS The correlation between the automatic and dermatologist counts was 0.62 on the face and 0.51 on the arms, compared to the dermatologist's intra-observer variation of 0.83 and 0.93 for the same. Sensitivity of automatic detection was 39.5% on the face, 53.1% on the arms. Positive predictive values were 13.9% on the face and 39.8% on the arms. Significantly more lesions (p<0.0001) were detected in the high actinic keratosis group compared to the no actinic keratosis group. CONCLUSIONS The proposed method was inferior to assessment by the dermatologist in terms of sensitivity and positive predictive value. However, this pilot study used only a single simple feature and was still able to achieve sensitivity of detection of 53.1% on the arms.This suggests that image analysis is a feasible avenue of investigation for overcoming variability in clinical assessment. Future studies should focus on more sophisticated features to improve sensitivity for actinic keratoses without erythema and limit false positives associated with the anatomical structures on the face.
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Affiliation(s)
- Samuel C. Hames
- Dermatology Research Center, School of Medicine, University of Queensland, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Australia
| | - Sudipta Sinnya
- Dermatology Research Center, School of Medicine, University of Queensland, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Australia
| | - Jean-Marie Tan
- Dermatology Research Center, School of Medicine, University of Queensland, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Australia
| | - Conrad Morze
- Dermatology Research Center, School of Medicine, University of Queensland, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Australia
| | - Azadeh Sahebian
- Dermatology Research Center, School of Medicine, University of Queensland, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Australia
| | - H. Peter Soyer
- Dermatology Research Center, School of Medicine, University of Queensland, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Australia
| | - Tarl W. Prow
- Dermatology Research Center, School of Medicine, University of Queensland, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Australia
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Automated delineation of dermal-epidermal junction in reflectance confocal microscopy image stacks of human skin. J Invest Dermatol 2014; 135:710-717. [PMID: 25184959 DOI: 10.1038/jid.2014.379] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Revised: 07/25/2014] [Accepted: 08/07/2014] [Indexed: 11/08/2022]
Abstract
Reflectance confocal microscopy (RCM) images skin noninvasively, with optical sectioning and nuclear-level resolution comparable with that of pathology. On the basis of the assessment of the dermal-epidermal junction (DEJ) and morphologic features in its vicinity, skin cancer can be diagnosed in vivo with high sensitivity and specificity. However, the current visual, qualitative approach for reading images leads to subjective variability in diagnosis. We hypothesize that machine learning-based algorithms may enable a more quantitative, objective approach. Testing and validation were performed with two algorithms that can automatically delineate the DEJ in RCM stacks of normal human skin. The test set was composed of 15 fair- and 15 dark-skin stacks (30 subjects) with expert labelings. In dark skin, in which the contrast is high owing to melanin, the algorithm produced an average error of 7.9±6.4 μm. In fair skin, the algorithm delineated the DEJ as a transition zone, with average error of 8.3±5.8 μm for the epidermis-to-transition zone boundary and 7.6±5.6 μm for the transition zone-to-dermis. Our results suggest that automated algorithms may quantitatively guide the delineation of the DEJ, to assist in objective reading of RCM images. Further development of such algorithms may guide assessment of abnormal morphological features at the DEJ.
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Champin J, Perrot JL, Cinotti E, Labeille B, Douchet C, Parrau G, Cambazard F, Seguin P, Alix T. In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna. Dermatol Surg 2014; 40:247-56. [PMID: 24447286 DOI: 10.1111/dsu.12432] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Lentigo maligna (LM) is a therapeutic challenge for surgeons because of its location in aesthetic areas and the difficulty in determining margins. OBJECTIVE To investigate a new procedure combining the "spaghetti" technique described by Gaudy-Marqueste and colleagues in 2011 with in vivo reflectance confocal microscopy (RCM) to define the margins of LM more accurately and allow strict histologic control. METHODS AND MATERIALS Thirty-three consecutive patients with LM of the head underwent a RCM-guided delineation of the margins followed by the "spaghetti" technique. RESULTS The excision of the first "spaghetti" in a tumor-free area was obtained in 28 of 33 patients. In the other five cases, persistence of LM foci was found in <5% of the length of spaghetti. The average number of pieces of "spaghetti" was 1.2 (range 1-3). Definitive histologic examination of the lesion showed a minimum average margin of 2.7 mm. Follow-up in 27 patients after an average of 10 months (range 4-25 months) did not show any recurrence. CONCLUSION This procedure allows accurate definition of the surgical margins of LM, with a low rate of multiple excisions, sparing tissue in functional and aesthetic areas. These results should be confirmed on the basis of a larger series with longer follow-up.
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Affiliation(s)
- Julie Champin
- Department of Maxillofacial and Plastic Surgery, University Hospital of Saint Étienne, Saint-Étienne, France; Faculty of Medicine, University of Saint-Étienne, Saint-Étienne, France
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Kupetsky EA, Ferris LK. The diagnostic evaluation of MelaFind multi-spectral objective computer vision system. ACTA ACUST UNITED AC 2013; 7:405-11. [DOI: 10.1517/17530059.2013.785520] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Abstract
Reflectance confocal microscopy (RCM) enables the noninvasive in vivo imaging of the skin with a horizontal axis and a cellular-level resolution allowing the study of the skin from superficial layers to papillary dermis. It has arisen an important tool in the study of tumors and specially an important role in the characterization of melanoma. Melanocytic lesions present a large number of characteristic findings visible in upper parts of the tumors, such as in the case of melanoma: pagetoid roundish or dendritic cells in superficial epidermis, atypical nests at the dermoepidermal junction, nonedged papillae and atypical nucleated cells in papillary dermis. Several studies have demonstrated that RCM may improve the accuracy in the differentiation of benign and malignant melanocytic lesions as an adjuvant technique to dermoscopy, and three main algorithms have been developed to apply in equivocal lesions. The advantage of in vivo observation in real time of the tumor at the bedside is opening the clinical applications of RCM in the evaluation of melanocytic lesions, and in particular in the study of facial maculae and lentigo maligna melanoma, amelanotic melanoma, and management of subclinical margins, recurrences, or monitoring noninvasive treatment of tumors.
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Affiliation(s)
- Cristina Carrera
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Spain
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Abstract
Detection of melanoma at an early stage is crucial to improving survival rates in melanoma. Accurate diagnosis by current techniques including dermatoscopy remains difficult, and new tools are needed to improve our diagnostic abilities. This article discusses recent advances in diagnostic techniques including confocal scanning laser microscopy, MelaFind, SIAscopy, and noninvasive genomic detection, as well as other future possibilities to aid in diagnosing melanoma. Advantages and barriers to implementation of the various technologies are also discussed.
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Affiliation(s)
- Laura Korb Ferris
- Department of Dermatology, University of Pittsburgh Medical Center, 3601 Fifth Avenue, Pittsburgh, PA 15213, USA.
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36
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O'Donnell AT, Kim CC. Update and Clinical Use of Imaging Technologies for Pigmented Lesions of the Skin. ACTA ACUST UNITED AC 2012; 31:38-44. [DOI: 10.1016/j.sder.2011.12.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2011] [Revised: 12/05/2011] [Accepted: 12/05/2011] [Indexed: 11/28/2022]
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Kurugol S, Rajadhyaksha M, Dy JG, Brooks DH. Validation Study of Automated Dermal/Epidermal Junction Localization Algorithm in Reflectance Confocal Microscopy Images of Skin. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2012; 8207. [PMID: 24376908 DOI: 10.1117/12.909227] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Reflectance confocal microscopy (RCM) has seen increasing clinical application for noninvasive diagnosis of skin cancer. Identifying the location of the dermal-epidermal junction (DEJ) in the image stacks is key for effective clinical imaging. For example, one clinical imaging procedure acquires a dense stack of 0.5×0.5mm FOV images and then, after manual determination of DEJ depth, collects a 5×5mm mosaic at that depth for diagnosis. However, especially in lightly pigmented skin, RCM images have low contrast at the DEJ which makes repeatable, objective visual identification challenging. We have previously published proof of concept for an automated algorithm for DEJ detection in both highly- and lightly-pigmented skin types based on sequential feature segmentation and classification. In lightly-pigmented skin the change of skin texture with depth was detected by the algorithm and used to locate the DEJ. Here we report on further validation of our algorithm on a more extensive collection of 24 image stacks (15 fair skin, 9 dark skin). We compare algorithm performance against classification by three clinical experts. We also evaluate inter-expert consistency among the experts. The average correlation across experts was 0.81 for lightly pigmented skin, indicating the difficulty of the problem. The algorithm achieved epidermis/dermis misclassification rates smaller than 10% (based on 25×25 mm tiles) and average distance from the expert labeled boundaries of ~6.4 μm for fair skin and ~5.3 μm for dark skin, well within average cell size and less than 2x the instrument resolution in the optical axis.
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Affiliation(s)
- Sila Kurugol
- Electrical and Comp. Eng., Northeastern University, 360 Huntington Av., Boston, MA
| | - Milind Rajadhyaksha
- Dermatology Service, Memorial Sloan Kettering Cancer Cnt., 160 East 53 St., New York, NY
| | - Jennifer G Dy
- Electrical and Comp. Eng., Northeastern University, 360 Huntington Av., Boston, MA
| | - Dana H Brooks
- Electrical and Comp. Eng., Northeastern University, 360 Huntington Av., Boston, MA
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Kurugol S, Dy JG, Brooks DH, Rajadhyaksha M. Pilot study of semiautomated localization of the dermal/epidermal junction in reflectance confocal microscopy images of skin. JOURNAL OF BIOMEDICAL OPTICS 2011; 16:036005. [PMID: 21456869 PMCID: PMC3077965 DOI: 10.1117/1.3549740] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2010] [Revised: 01/07/2011] [Accepted: 01/10/2011] [Indexed: 05/21/2023]
Abstract
Reflectance confocal microscopy (RCM) continues to be translated toward the detection of skin cancers in vivo. Automated image analysis may help clinicians and accelerate clinical acceptance of RCM. For screening and diagnosis of cancer, the dermal/epidermal junction (DEJ), at which melanomas and basal cell carcinomas originate, is an important feature in skin. In RCM images, the DEJ is marked by optically subtle changes and features and is difficult to detect purely by visual examination. Challenges for automation of DEJ detection include heterogeneity of skin tissue, high inter-, intra-subject variability, and low optical contrast. To cope with these challenges, we propose a semiautomated hybrid sequence segmentation/classification algorithm that partitions z-stacks of tiles into homogeneous segments by fitting a model of skin layer dynamics and then classifies tile segments as epidermis, dermis, or transitional DEJ region using texture features. We evaluate two different training scenarios: 1. training and testing on portions of the same stack; 2. training on one labeled stack and testing on one from a different subject with similar skin type. Initial results demonstrate the detectability of the DEJ in both scenarios with epidermis/dermis misclassification rates smaller than 10% and average distance from the expert labeled boundaries around 8.5 μm.
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Affiliation(s)
- Sila Kurugol
- Northeastern University, Electrical and Computer Engineering, Boston, Massachusetts 02115, USA.
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Kurugol S, Dy JG, Rajadhyaksha M, Gossage KW, Weissman J, Brooks DH. Semi-automated Algorithm for Localization of Dermal/ Epidermal Junction in Reflectance Confocal Microscopy Images of Human Skin. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2011; 7904:7901A. [PMID: 21709746 PMCID: PMC3120112 DOI: 10.1117/12.875392] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The examination of the dermis/epidermis junction (DEJ) is clinically important for skin cancer diagnosis. Reflectance confocal microscopy (RCM) is an emerging tool for detection of skin cancers in vivo. However, visual localization of the DEJ in RCM images, with high accuracy and repeatability, is challenging, especially in fair skin, due to low contrast, heterogeneous structure and high inter- and intra-subject variability. We recently proposed a semi-automated algorithm to localize the DEJ in z-stacks of RCM images of fair skin, based on feature segmentation and classification. Here we extend the algorithm to dark skin. The extended algorithm first decides the skin type and then applies the appropriate DEJ localization method. In dark skin, strong backscatter from the pigment melanin causes the basal cells above the DEJ to appear with high contrast. To locate those high contrast regions, the algorithm operates on small tiles (regions) and finds the peaks of the smoothed average intensity depth profile of each tile. However, for some tiles, due to heterogeneity, multiple peaks in the depth profile exist and the strongest peak might not be the basal layer peak. To select the correct peak, basal cells are represented with a vector of texture features. The peak with most similar features to this feature vector is selected. The results show that the algorithm detected the skin types correctly for all 17 stacks tested (8 fair, 9 dark). The DEJ detection algorithm achieved an average distance from the ground truth DEJ surface of around 4.7μm for dark skin and around 7-14μm for fair skin.
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Affiliation(s)
- Sila Kurugol
- Electrical and Comp. Eng., Northeastern University, 360 Huntington Av., Boston, MA
| | - Jennifer G. Dy
- Electrical and Comp. Eng., Northeastern University, 360 Huntington Av., Boston, MA
| | - Milind Rajadhyaksha
- Dermatology Service, Memorial Sloan Kettering Cancer Cnt., 160 East 53 St., New York, NY
| | - Kirk W. Gossage
- Unilever Research and Development, 40 Merritt Blvd., Trumbull, CT
| | - Jesse Weissman
- Unilever Research and Development, 40 Merritt Blvd., Trumbull, CT
| | - Dana H. Brooks
- Electrical and Comp. Eng., Northeastern University, 360 Huntington Av., Boston, MA
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