  Automated segmentation of individual calf muscle compartments from 3D
magnetic resonance (MR) images is essential for developing quantitative
biomarkers for muscular disease progression and its prediction. Achieving
clinically acceptable results is a challenging task due to large variations in
muscle shape and MR appearance. Although deep convolutional neural networks
(DCNNs) achieved improved accuracy in various image segmentation tasks, certain
problems such as utilizing long-range information and incorporating high-level
constraints remain unsolved. We present a novel fully convolutional network
(FCN), called FilterNet, that utilizes contextual information in a large
neighborhood and embeds edge-aware constraints for individual calf muscle
compartment segmentations. An encoder-decoder architecture with flexible
backbone blocks is used to systematically enlarge convolution receptive field
and preserve information at all resolutions. Edge positions derived from the
FCN output muscle probability maps are explicitly regularized using
kernel-based edge detection in an end-to-end optimization framework. Our
FilterNet was evaluated on 40 T1-weighted MR images of 10 healthy and 30
diseased subjects by 4-fold cross-validation. Mean DICE coefficients of
88.00%--91.29% and mean absolute surface positioning errors of 1.04--1.66 mm
were achieved for the five 3D muscle compartments.
