We propose a method for automatic segmentation of individual muscles from a
clinical CT. The method uses Bayesian convolutional neural networks with the
U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric
in addition to the segmentation label. We evaluated the performance of the
proposed method using two data sets: 20 fully annotated CTs of the hip and
thigh regions and 18 partially annotated CTs that are publicly available from
The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice
coefficient (DC) of 0.891 +/- 0.016 (mean +/- std) and an average symmetric
surface distance (ASD) of 0.994 +/- 0.230 mm over 19 muscles in the set of 20
CTs. These results were statistically significant improvements compared to the
state-of-the-art hierarchical multi-atlas method which resulted in 0.845 +/-
0.031 DC and 1.556 +/- 0.444 mm ASD. We evaluated validity of the uncertainty
metric in the multi-class organ segmentation problem and demonstrated a
correlation between the pixels with high uncertainty and the segmentation
failure. One application of the uncertainty metric in active-learning is
demonstrated, and the proposed query pixel selection method considerably
reduced the manual annotation cost for expanding the training data set. The
proposed method allows an accurate patient-specific analysis of individual
muscle shapes in a clinical routine. This would open up various applications
including personalization of biomechanical simulation and quantitative
evaluation of muscle atrophy.