  Muscle volume is a useful quantitative biomarker in sports, but also for the
follow-up of degenerative musculo-skelletal diseases. In addition to volume,
other shape biomarkers can be extracted by segmenting the muscles of interest
from medical images. Manual segmentation is still today the gold standard for
such measurements despite being very time-consuming. We propose a method for
automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance
Images to assist such morphometric analysis. By their nature, the tissue of
different muscles is undistinguishable when observed in MR Images. Thus, muscle
segmentation algorithms cannot rely on appearance but only on contour cues.
However, such contours are hard to detect and their thickness varies across
subjects. To cope with the above challenges, we propose a segmentation approach
based on a hybrid architecture, combining convolutional and visual transformer
blocks. We investigate for the first time the behaviour of such hybrid
architectures in the context of muscle segmentation for shape analysis.
Considering the consistent anatomical muscle configuration, we rely on
transformer blocks to capture the longrange relations between the muscles. To
further exploit the anatomical priors, a second contribution of this work
consists in adding a regularisation loss based on an adjacency matrix of
plausible muscle neighbourhoods estimated from the training data. Our
experimental results on a unique database of elite athletes show it is possible
to train complex hybrid models from a relatively small database of large
volumes, while the anatomical prior regularisation favours better predictions.
