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Abstract #4308

Automatic Bone Segmentation for Shoulder MRI using Statistical Shape Models

Zhengyi Yang 1 , Jurgen Fripp 2 , Craig Engstrom 1 , Shekhar Chandra 2 , Ying Xia 2 , Anthony Paproki 2 , Mark Strudwick 1 , Ales Neubert 2 , and Stuart Crozier 1

1 University of Queensland, Brisbane, Queensland, Australia, 2 CSIRO, Brisbane, Queensland, Australia

In conditions such as shoulder osteoarthritis and impingement syndrome, it is important to quantify the subtle changes in the morphology of the glenohumeral cartilages, which can be measured from image segmentation. However, automatic cartilage segmentation from MR images is challenging. As a critical step stone, we present a fully automatic shoulder bone segmentation pipeline using statistical shape models. The mean volume overlap calculated as Dice Similarity Coefficient between automatic and manual segmentation is 0.94 and 0.76 for humerus and scapula, respectively. These promising results imply a high likelihood of the proposed pipeline being integrated into a fully automatic solution to shoulder cartilage segmentation and quantitative analysis on cartilage morphometry.

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