  Automatic segmentation of magnetic resonance (MR) images is crucial for
morphological evaluation of the pediatric musculoskeletal system in clinical
practice. However, the accuracy and generalization performance of individual
segmentation models are limited due to the restricted amount of annotated
pediatric data. Hence, we propose to train a segmentation model on multiple
datasets, arising from different parts of the anatomy, in a multi-task and
multi-domain learning framework. This approach allows to overcome the inherent
scarcity of pediatric data while benefiting from a more robust shared
representation. The proposed segmentation network comprises shared
convolutional filters, domain-specific batch normalization parameters that
compute the respective dataset statistics and a domain-specific segmentation
layer. Furthermore, a supervised contrastive regularization is integrated to
further improve generalization capabilities, by promoting intra-domain
similarity and impose inter-domain margins in embedded space. We evaluate our
contributions on two pediatric imaging datasets of the ankle and shoulder
joints for bone segmentation. Results demonstrate that the proposed model
outperforms state-of-the-art approaches.
