Radiol Artif Intell 2021 May 10;3(3):e200078. Epub 2021 Feb 10.
Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.) and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of Radiology, University of California, San Francisco, San Francisco, Calif (F.C., C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of Radiology, Northwestern University, Chicago, Ill (U.B.); Department of Radiology, Columbia University, New York, NY (S.J.); Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.); Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G., M.Y., X.L.); Department of Radiology, New York University Langone Health, New York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria (V.J.).
Purpose: To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Materials And Methods: A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set. Read More