  Automatic segmentation of pathological shoulder muscles in patients with
musculo-skeletal diseases is a challenging task due to the huge variability in
muscle shape, size, location, texture and injury. A reliable fully-automated
segmentation method from magnetic resonance images could greatly help
clinicians to plan therapeutic interventions and predict interventional
outcomes while eliminating time consuming manual segmentation efforts. The
purpose of this work is three-fold. First, we investigate the feasibility of
pathological shoulder muscle segmentation using deep learning techniques, given
a very limited amount of available annotated pediatric data. Second, we address
the learning transferability from healthy to pathological data by comparing
different learning schemes in terms of model generalizability. Third, extended
versions of deep convolutional encoder-decoder architectures using encoders
pre-trained on non-medical data are proposed to improve the segmentation
accuracy. Methodological aspects are evaluated in a leave-one-out fashion on a
dataset of 24 shoulder examinations from patients with obstetrical brachial
plexus palsy and focus on 4 different muscles including deltoid as well as
infraspinatus, supraspinatus and subscapularis from the rotator cuff. The most
relevant segmentation model is partially pre-trained on ImageNet and jointly
exploits inter-patient healthy and pathological annotated data. Its performance
reaches Dice scores of 82.4%, 82.0%, 71.0% and 82.8% for deltoid,
infraspinatus, supraspinatus and subscapularis muscles. Absolute surface
estimation errors are all below 83mm$^2$ except for supraspinatus with
134.6mm$^2$. These contributions offer new perspectives for force inference in
the context of musculo-skeletal disorder management.
