  Clinical diagnosis of the pediatric musculoskeletal system relies on the
analysis of medical imaging examinations. In the medical image processing
pipeline, semantic segmentation using deep learning algorithms enables an
automatic generation of patient-specific three-dimensional anatomical models
which are crucial for morphological evaluation. However, the scarcity of
pediatric imaging resources may result in reduced accuracy and generalization
performance of individual deep segmentation models. In this study, we propose
to design a novel multi-task, multi-domain learning framework in which a single
segmentation network is optimized over the union of multiple datasets arising
from distinct parts of the anatomy. Unlike previous approaches, we
simultaneously consider multiple intensity domains and segmentation tasks to
overcome the inherent scarcity of pediatric data while leveraging shared
features between imaging datasets. To further improve generalization
capabilities, we employ a transfer learning scheme from natural image
classification, along with a multi-scale contrastive regularization aimed at
promoting domain-specific clusters in the shared representations, and
multi-joint anatomical priors to enforce anatomically consistent predictions.
We evaluate our contributions for performing bone segmentation using three
scarce and pediatric imaging datasets of the ankle, knee, and shoulder joints.
Our results demonstrate that the proposed approach outperforms individual,
transfer, and shared segmentation schemes in Dice metric with statistically
sufficient margins. The proposed model brings new perspectives towards
intelligent use of imaging resources and better management of pediatric
musculoskeletal disorders.
