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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