Holistic Segmentation of Intermuscular Adipose Tissues on Thigh MRI
Muscular dystrophies (MD) cause muscles to gradually degenerate into fat. In order to effectively study and track disease progression, it is important to quantify both muscle and fat volumes, especially the intermuscular adipose tissue (IMAT). Existing methods were mostly based on unsupervised pixel clustering and morphological models. We propose a method integrating two holistic neural networks (one for edges and one for regions) and a dual active contour model to accurately locate the fascia lata and segment multiple tissue types on thigh MRIs. The proposed method is robust to image artifacts and weak boundaries, and thus it performs well for severe MD cases. Our method was tested on 104 data sets and achieved Dice coefficients 0.940 and 0.943 for muscle and IMAT in challenging severe cases, respectively.
KeywordsMuscular dystrophy MRI Holistic segmentation Muscle and fat segmentation
This research was supported by the National Institutes of Health, Clinical Center. The authors thank Nvidia for the GPU donation.
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