Segmentation of Knee Images: A Grand Challenge

In this paper, we present an evaluation framework for the 3D segmentation of knee bones and cartilage from magnetic resonance images. The framework was established for one of the three challenges at the “Medical Image Analysis for the Clinic: A Grand Challenge” workshop held at the 2010 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in Beijing, China. After this workshop, the framework will remain open to online submissions via www.ski10.org. We describe the motivation for this challenge, the preparation of training and test datasets, and the evaluation measures used to rate submitted

[1]  Claus Christiansen,et al.  Automatic quantification of local and global articular cartilage surface curvature: Biomarkers for osteoarthritis? , 2008, Magnetic resonance in medicine.

[2]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[3]  Stuart Crozier,et al.  Automatic Segmentation and Quantitative Analysis of the Articular Cartilages From Magnetic Resonance Images of the Knee , 2010, IEEE Transactions on Medical Imaging.

[4]  F. Eckstein,et al.  Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): morphological assessment. , 2006, Osteoarthritis and cartilage.

[5]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[6]  Martin Styner,et al.  Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms , 2009, Medical Image Anal..

[7]  Paul J. Rullkoetter,et al.  Development of subject-specific and statistical shape models of the knee using an efficient segmentation and mesh-morphing approach , 2010, Comput. Methods Programs Biomed..

[8]  Prevalence of disabilities and associated health conditions among adults--United States, 1999. , 2001, MMWR. Morbidity and mortality weekly report.

[9]  Ilaria Gori,et al.  Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study , 2010, Medical Image Anal..