• Artificial Intelligence
• Brain
• Brain stroke
• Computed Tomography
• Convolutional Neural Networks

• Humans
• Deep Learning
• Tomography, X-Ray Computed/methods
• Stroke/diagnostic imaging
• Stroke/classification
• Retrospective Studies
• Male
• Female
• Middle Aged
• Aged
• Reproducibility of Results
• Radiographic Image Interpretation, Computer-Assisted/methods
• Sensitivity and Specificity
• Aged, 80 and over
• Adult

• CT volumes
• attention mechanism
• liver segmentation
• multi‐scale feature fusion

• Humans
• Liver/diagnostic imaging
• Imaging, Three-Dimensional/methods
• Tomography, X-Ray Computed/methods
• Algorithms
• Image Processing, Computer-Assisted/methods

• 20210101167JC Natural Science Foundation of Jilin Province of China

• artificial intelligence
• deep learning
• diagnosis
• hepatocellular carcinoma
• liver cancer
• machine learning
• medical imaging
• prediction

• Deep learning
• Image segmentation
• Liver
• Magnetic resonance imaging
• Positron emission tomography computed tomography

• Deep Learning
• Humans
• Magnetic Resonance Imaging
• Liver/diagnostic imaging
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/pathology
• Image Processing, Computer-Assisted
• Female
• Male
• Imaging, Three-Dimensional
• Middle Aged

• 958298 National Institute for Medical Research Development

• CNN
• MLP
• UNet
• lightweight network
• medical image segmentation

• 20XLB035 Chongqing Normal University

Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of computed tomography (CT) for early detection of liver cancer could save millions of lives per year. There is also an urgent need for a computerized method to interpret, detect and analyze CT scans reliably, easily, and correctly. However, precise segmentation of minute tumors is a difficult task because of variation in the shape, intensity, size, low contrast of the tumor, and the adjacent tissues of the liver. To address these concerns, a model comprised of three parts: synthetic image generation, localization, and segmentation, is proposed. An optimized generative adversarial network (GAN) is utilized for generation of synthetic images. The generated images are localized by using the improved localization model, in which deep features are extracted from pre-trained Resnet-50 models and fed into a YOLOv3 detector as an input. The proposed modified model localizes and classifies the minute liver tumor with 0.99 mean average precision (mAp). The third part is segmentation, in which pre-trained Inceptionresnetv2 employed as a base-Network of Deeplabv3 and subsequently is trained on fine-tuned parameters with annotated ground masks. The experiments reflect that the proposed approach has achieved greater than 95% accuracy in the testing phase and it is proven that, in comparison to the recently published work in this domain, this research has localized and segmented the liver and minute liver tumor with more accuracy.

• aortic valve
• computed tomography
• seep learning
• segmentation

Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare systems. Artificial intelligence (AI) algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation. When applied to liver MRI, they have the potential to improve clinical decision making, and increase throughput by automating analyses. With Liver diseases becoming more prevalent in society, the need to implement these techniques to utilize liver MRI to its full potential, is paramount. In this review, we report on the current methods and applications of AI methods in liver MRI, with a focus on machine learning and deep learning methods. We assess four main themes of segmentation, classification, image synthesis and artefact detection, and their respective potential in liver MRI and the wider clinic. We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field. Though there are many hurdles to overcome in implementing AI methods in the clinic, we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.

• Liver diseases
• Magnetic resonance imaging
• Machine learning
• Deep learning
• Artificial intelligence
• Computer vision

• Algorithms
• Artificial Intelligence
• Humans
• Liver
• Machine Learning
• Magnetic Resonance Imaging

To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessels from the liver region. However, only user-defined liver region remained a manual process. Therefore, this work aimed to develop an automated liver region segmentation technique to automate the whole process of median LIC calculation.

553 MR examinations from 471 thalassemia major patients were used in this study. LIC maps (in mg/g dry weight) were calculated and used as the input of segmentation procedures. Anatomical landmark data were detected and used to restrict ROI. After that, the liver region was segmented using fuzzy c-means clustering and reduced segmentation errors by morphological processes. According to the clinical application, erosion with a suitable size of the structuring element was applied to reduce the segmented liver region to avoid uncertainty around the edge of the liver. The segmentation results were evaluated by comparing with manual segmentation performed by a board-certified radiologist.

The proposed method was able to produce a good grade output in approximately 81% of all data. Approximately 11% of all data required an easy modification step. The rest of the output, approximately 8%, was an unsuccessful grade and required manual intervention by a user. For the evaluation matrices, percent dice similarity coefficient (%DSC) was in the range 86–92, percent Jaccard index (%JC) was 78–86, and Hausdorff distance (H) was 14–28 mm, respectively. In this study, percent false positive (%FP) and percent false negative (%FN) were applied to evaluate under- and over-segmentation that other evaluation matrices could not handle. The average of operation times could be reduced from 10 s per case using traditional method, to 1.5 s per case using our proposed method.

The experimental results showed that the proposed method provided an effective automated liver segmentation technique, which can be applied clinically for automated median LIC calculation in thalassemia major patients.

• Anatomical landmark data
• Fuzzy c-means (FCM) clustering
• Liver iron concentration (LIC)
• Liver segmentation
• Magnetic resonance image (MRI)

• MRI sequences
• atrial geometry
• semantic segmentation
• supervised domain adaptation

• IDIFEDER/2020/030 Program ERDF of the Valencian Community 2014-2020 ,European Union

Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD.

The performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interobserver correlation coefficient (ICC), and Bland–Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC.

The DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry.

PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings.

• Artificial intelligence
• Polycystic kidney diseases
• Tomography

• artificial neural network
• classification
• feature selection
• optimal kernel K-means clustering
• pre-processing

• deep learning
• detection
• diffusion weighted MRI
• dynamic contrast-enhanced MRI
• liver
• segmentation

• Computed tomography
• Feature extractor
• Radiological density
• Stroke
• Stroke rating

• Algorithms
• Bayes Theorem
• Brain/diagnostic imaging
• Humans
• Skull/diagnostic imaging
• Stroke/diagnostic imaging
• Tomography, X-Ray Computed

• Fully automated segmentation scheme
• Liver volumetry
• MR liver volumetry
• Quantitative radiology
• Transplantation

Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow processing speed, particularly for images in the top and bottom of the lung and the images that contain lung nodules.

Our proposed method first uses the position of the lung parenchyma image features to obtain lung parenchyma ROI image sequences. A gradient and sequential linear iterative clustering algorithm (GSLIC) for sequence image segmentation is then proposed to segment the ROI image sequences and obtain superpixel samples. The SGNF, which is optimized by a genetic algorithm (GA), is then utilized for superpixel clustering. Finally, the grey and geometric features of the superpixel samples are used to identify and segment all of the lung parenchyma image sequences.

Our proposed method achieves higher segmentation precision and greater accuracy in less time. It has an average processing time of 42.21 seconds for each dataset and an average volume pixel overlap ratio of 92.22 ± 4.02% for four types of lung parenchyma image sequences.

• Algorithms
• Cluster Analysis
• Diagnosis, Computer-Assisted/methods
• Diagnosis, Computer-Assisted/statistics & numerical data
• Humans
• Image Interpretation, Computer-Assisted/methods
• Lung/diagnostic imaging
• Lung/pathology
• Lung Neoplasms/diagnostic imaging
• Lung Neoplasms/pathology
• Pattern Recognition, Automated/methods
• Pattern Recognition, Automated/statistics & numerical data
• Tomography, X-Ray Computed/statistics & numerical data

• National Natural Science Foundation of China (CN)
• National Natural Science Foundation of China
• National Key Laboratory Open Foundation of China

• Active surface
• Liver segmentation
• Magnetic resonance imaging
• Multichannel
• Multivariate image descriptors
• Variational techniques

• Humans
• Liver/diagnostic imaging
• Magnetic Resonance Imaging/methods
• Surface Properties

• Algorithms
• Fluorodeoxyglucose F18
• Humans
• Lung Neoplasms/diagnostic imaging
• Positron-Emission Tomography
• Radiopharmaceuticals
• Sensitivity and Specificity
• Support Vector Machine
• Tomography, X-Ray Computed

• Active contour
• Liver segmentation
• SPIR
• Signed pressure force function
• Variational level set

This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.

• Liver segmentation
• Magnetic resonance imaging
• Mathematical morphology
• Stochastic partitions
• Watershed transform

• Algorithms
• Humans
• Liver/anatomy & histology
• Magnetic Resonance Imaging/methods
• Stochastic Processes

Abdominal organs segmentation of magnetic resonance (MR) images is an important but challenging task in medical image processing. Especially for abdominal tissues or organs, such as liver and kidney, MR imaging is a very difficult task due to the fact that MR images are affected by intensity inhomogeneity, weak boundary, noise and the presence of similar objects close to each other.

In this study, a novel method for tissue or organ segmentation in abdomen MR imaging is proposed; this method combines kernel graph cuts (KGC) with shape priors. First, the region growing algorithm and morphology operations are used to obtain the initial contour. Second, shape priors are obtained by training the shape templates, which were collected from different human subjects with kernel principle component analysis (KPCA) after the registration between all the shape templates and the initial contour. Finally, a new model is constructed by integrating the shape priors into the kernel graph cuts energy function. The entire process aims to obtain an accurate image segmentation.

The proposed segmentation method has been applied to abdominal organs MR images. The results showed that a satisfying segmentation without boundary leakage and segmentation incorrect can be obtained also in presence of similar tissues. Quantitative experiments were conducted for comparing the proposed segmentation with other three methods: DRLSE, initial erosion contour and KGC without shape priors. The comparison is based on two quantitative performance measurements: the probabilistic rand index (PRI) and the variation of information (VoI). The proposed method has the highest PRI value (0.9912, 0.9983 and 0.9980 for liver, right kidney and left kidney respectively) and the lowest VoI values (1.6193, 0.3205 and 0.3217 for liver, right kidney and left kidney respectively).

The proposed method can overcome boundary leakage. Moreover it can segment liver and kidneys in abdominal MR images without segmentation errors due to the presence of similar tissues. The shape priors based on KPCA was integrated into fully automatic graph cuts algorithm (KGC) to make the segmentation algorithm become more robust and accurate. Furthermore, if a shelter is placed onto the target boundary, the proposed method can still obtain satisfying segmentation results.