  Automatic segmentation of the musculoskeletal system in pediatric magnetic
resonance (MR) images is a challenging but crucial task for morphological
evaluation in clinical practice. We propose a deep learning-based regularized
segmentation method for multi-structure bone delineation in MR images, designed
to overcome the inherent scarcity and heterogeneity of pediatric data. Based on
a newly devised shape code discriminator, our adversarial regularization scheme
enforces the deep network to follow a learnt shape representation of the
anatomy. The novel shape priors based adversarial regularization (SPAR)
exploits latent shape codes arising from ground truth and predicted masks to
guide the segmentation network towards more consistent and plausible
predictions. Our contribution is compared to state-of-the-art regularization
methods on two pediatric musculoskeletal imaging datasets from ankle and
shoulder joints.
