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For: Desai AD, Caliva F, Iriondo C, Mortazi A, Jambawalikar S, Bagci U, Perslev M, Igel C, Dam EB, Gaj S, Yang M, Li X, Deniz CM, Juras V, Regatte R, Gold GE, Hargreaves BA, Pedoia V, Chaudhari AS; IWOAI Segmentation Challenge Writing Group. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Radiol Artif Intell 2021;3:e200078. [PMID: 34235438 DOI: 10.1148/ryai.2021200078] [Cited by in Crossref: 17] [Cited by in F6Publishing: 20] [Article Influence: 8.5] [Reference Citation Analysis]
Number Citing Articles
1 Hirvasniemi J, Runhaar J, van der Heijden RA, Zokaeinikoo M, Yang M, Li X, Tan J, Rajamohan HR, Zhou Y, Deniz CM, Caliva F, Iriondo C, Lee JJ, Liu F, Martinez AM, Namiri N, Pedoia V, Panfilov E, Bayramoglu N, Nguyen HH, Nieminen MT, Saarakkala S, Tiulpin A, Lin E, Li A, Li V, Dam EB, Chaudhari AS, Kijowski R, Bierma-Zeinstra S, Oei EHG, Klein S. The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images. Osteoarthritis Cartilage 2023;31:115-25. [PMID: 36243308 DOI: 10.1016/j.joca.2022.10.001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Rodriguez-Vila B, Gonzalez-Hospital V, Puertas E, Beunza JJ, Pierce DM. Democratization of deep learning for segmenting cartilage from MRIs of human knees: Application to data from the osteoarthritis initiative. J Orthop Res 2022. [PMID: 36573479 DOI: 10.1002/jor.25509] [Reference Citation Analysis]
3 Hayashi D, Roemer FW, Link T, Li X, Kogan F, Segal NA, Omoumi P, Guermazi A. Latest advancements in imaging techniques in OA. Ther Adv Musculoskelet Dis 2022;14:1759720X221146621. [PMID: 36601087 DOI: 10.1177/1759720X221146621] [Reference Citation Analysis]
4 Mallio CA, Bernetti C, Agostini F, Mangone M, Paoloni M, Santilli G, Martina FM, Quattrocchi CC, Zobel BB, Bernetti A. Advanced MR Imaging for Knee Osteoarthritis: A Review on Local and Brain Effects. Diagnostics (Basel) 2022;13. [PMID: 36611346 DOI: 10.3390/diagnostics13010054] [Reference Citation Analysis]
5 Sengar SS, Meulengracht C, Boesen MP, Overgaard AF, Gudbergsen H, Nybing JD, Perslev M, Dam EB. Multi‐planar 3D knee MRI segmentation via UNet inspired architectures. Int J Imaging Syst Tech 2022. [DOI: 10.1002/ima.22836] [Reference Citation Analysis]
6 Wirth W, Ladel C, Maschek S, Wisser A, Eckstein F, Roemer F. Quantitative measurement of cartilage morphology in osteoarthritis: current knowledge and future directions. Skeletal Radiol 2022. [DOI: 10.1007/s00256-022-04228-w] [Reference Citation Analysis]
7 Wu C, Zhou X, Chen G. Coarse-to-Fine Tranformer for articular disc of the temporomandibular joint Segmentation. 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) 2022. [DOI: 10.1109/cisp-bmei56279.2022.9980221] [Reference Citation Analysis]
8 Keles E, Irmakci I, Bagci U. Musculoskeletal MR Image Segmentation with Artificial Intelligence. Advances in Clinical Radiology 2022;4:179-188. [DOI: 10.1016/j.yacr.2022.04.010] [Reference Citation Analysis]
9 Schmidt AM, Desai AD, Watkins LE, Crowder HA, Black MS, Mazzoli V, Rubin EB, Lu Q, MacKay JW, Boutin RD, Kogan F, Gold GE, Hargreaves BA, Chaudhari AS. Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry. J Magn Reson Imaging 2022. [PMID: 35852498 DOI: 10.1002/jmri.28365] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Kessler DA, Mackay JW, Mcdonnell SM, Janiczek RL, Graves MJ, Kaggie JD, Gilbert FJ. Segmentation of knee MRI data with convolutional neural networks for semi-automated three-dimensional surface-based analysis of cartilage morphology and composition. Osteoarthritis Imaging 2022;2:100010. [DOI: 10.1016/j.ostima.2022.100010] [Reference Citation Analysis]
11 Harkey MS, Michel N, Kuenze C, Fajardo R, Salzler M, Driban JB, Hacihaliloglu I. Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images. CARTILAGE 2022;13:194760352210930. [DOI: 10.1177/19476035221093069] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Bodkin SG, Smith AC, Bergman BC, Huo D, Weber KA, Zarini S, Kahn D, Garfield A, Macias E, Harris-love MO. Utilization of Mid-Thigh Magnetic Resonance Imaging to Predict Lean Body Mass and Knee Extensor Strength in Obese Adults. Front Rehabilit Sci 2022;3. [DOI: 10.3389/fresc.2022.808538] [Reference Citation Analysis]
13 Ito S, Mine Y, Yoshimi Y, Takeda S, Tanaka A, Onishi A, Peng TY, Nakamoto T, Nagasaki T, Kakimoto N, Murayama T, Tanimoto K. Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning. Sci Rep 2022;12:221. [PMID: 34997167 DOI: 10.1038/s41598-021-04354-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Zhang P, Zhang RX, Chen XS, Zhou XY, Raithel E, Cui JL, Zhao J. Clinical validation of the use of prototype software for automatic cartilage segmentation to quantify knee cartilage in volunteers. BMC Musculoskelet Disord 2022;23:19. [PMID: 34980107 DOI: 10.1186/s12891-021-04973-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Perslev M, Pai A, Runhaar J, Igel C, Dam EB. Cross-Cohort Automatic Knee MRI Segmentation With Multi-Planar U-Nets. J Magn Reson Imaging 2021. [PMID: 34918423 DOI: 10.1002/jmri.27978] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
16 Calivà F, Namiri NK, Dubreuil M, Pedoia V, Ozhinsky E, Majumdar S. Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging. Nat Rev Rheumatol 2021. [PMID: 34848883 DOI: 10.1038/s41584-021-00719-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
17 Latif MHA, Faye I. Automated tibiofemoral joint segmentation based on deeply supervised 2D-3D ensemble U-Net: Data from the Osteoarthritis Initiative. Artif Intell Med 2021;122:102213. [PMID: 34823835 DOI: 10.1016/j.artmed.2021.102213] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
18 Oei EHG, Hirvasniemi J, van Zadelhoff TA, van der Heijden RA. Osteoarthritis year in review 2021: imaging. Osteoarthritis Cartilage 2021:S1063-4584(21)00974-2. [PMID: 34838670 DOI: 10.1016/j.joca.2021.11.012] [Reference Citation Analysis]
19 Gokyar S, Robb FJL, Kainz W, Chaudhari A, Winkler SA. MRSaiFE: An AI-based Approach Towards the Real-Time Prediction of Specific Absorption Rate. IEEE Access 2021;9:140824-34. [PMID: 34722096 DOI: 10.1109/access.2021.3118290] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
20 Oei EHG, van Zadelhoff TA, Eijgenraam SM, Klein S, Hirvasniemi J, van der Heijden RA. 3D MRI in Osteoarthritis. Semin Musculoskelet Radiol 2021;25:468-79. [PMID: 34547812 DOI: 10.1055/s-0041-1730911] [Reference Citation Analysis]
21 Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
22 Weber KA 2nd, Abbott R, Bojilov V, Smith AC, Wasielewski M, Hastie TJ, Parrish TB, Mackey S, Elliott JM. Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions. Sci Rep 2021;11:16567. [PMID: 34400672 DOI: 10.1038/s41598-021-95972-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
23 Panfilov E, Tiulpin A, Nieminen MT, Saarakkala S, Casula V. Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative. J Orthop Res 2021. [PMID: 34324223 DOI: 10.1002/jor.25150] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]