Combining Shape Priors with Conditional Adversarial Networks for Improved Scapula Segmentation in MR images
This work addresses the problem of accurate scapula segmentation for pre-operative management of musculoskeletal diseases, representing an incremental advance in medical imaging.
The paper tackled scapula bone segmentation in MR images by incorporating anatomical shape priors into a conditional adversarial network, achieving significant improvements over UNet and its derivatives on a dataset of 15 pediatric shoulder examinations.
This paper proposes an automatic method for scapula bone segmentation from Magnetic Resonance (MR) images using deep learning. The purpose of this work is to incorporate anatomical priors into a conditional adversarial framework, given a limited amount of heterogeneous annotated images. Our approach encourages the segmentation model to follow the global anatomical properties of the underlying anatomy through a learnt non-linear shape representation while the adversarial contribution refines the model by promoting realistic delineations. These contributions are evaluated on a dataset of 15 pediatric shoulder examinations, and compared to state-of-the-art architectures including UNet and recent derivatives. The significant improvements achieved bring new perspectives for the pre-operative management of musculo-skeletal diseases.