  Morphological and diagnostic evaluation of pediatric musculoskeletal system
is crucial in clinical practice. However, most segmentation models do not
perform well on scarce pediatric imaging data. We propose a new pre-trained
regularized convolutional encoder-decoder network for the challenging task of
segmenting heterogeneous pediatric magnetic resonance (MR) images. To this end,
we have conceived a novel optimization scheme for the segmentation network
which comprises additional regularization terms to the loss function. In order
to obtain globally consistent predictions, we incorporate a shape priors based
regularization, derived from a non-linear shape representation learnt by an
auto-encoder. Additionally, an adversarial regularization computed by a
discriminator is integrated to encourage precise delineations. The proposed
method is evaluated for the task of multi-bone segmentation on two scarce
pediatric imaging datasets from ankle and shoulder joints, comprising
pathological as well as healthy examinations. The proposed method performed
either better or at par with previously proposed approaches for Dice,
sensitivity, specificity, maximum symmetric surface distance, average symmetric
surface distance, and relative absolute volume difference metrics. We
illustrate that the proposed approach can be easily integrated into various
bone segmentation strategies and can improve the prediction accuracy of models
pre-trained on large non-medical images databases. The obtained results bring
new perspectives for the management of pediatric musculoskeletal disorders.
