Probabilistic multi-shape segmentation of knee extensor and flexor muscles

Authors:
Shawn Andrews;Ghassan Hamarneh;Azadeh Yazdanpanah;Bahareh HajGhanbari;W. Darlene Reid
Affiliations:
Medical Image Analysis Lab, Simon Fraser University, Canada;Medical Image Analysis Lab, Simon Fraser University, Canada;Medical Image Analysis Lab, Simon Fraser University, Canada;Department of Physical Therapy, University of British Columbia, Canada;Department of Physical Therapy, University of British Columbia, Canada
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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Abstract

Patients with chronic obstructive pulmonary disease (COPD) often exhibit skeletal muscle weakness in lower limbs. Analysis of the shapes and sizes of these muscles can lead to more effective therapy. Unfortunately, segmenting these muscles from one another is a challenging task due to a lack of image information in many areas. We present a fully automatic segmentation method that overcomes the inherent difficulties of this problem to accurately segment the different muscles. Our method enforces a multi-region shape prior on the segmentation to ensure feasibility and provides an energy minimizing probabilistic segmentation that indicates areas of uncertainty. Our experiments on 3D MRI datasets yield an average Dice similarity coefficient of 0.92 ± 0.03 with the ground truth.