# Combining Shape Priors with Conditional Adversarial Networks for Improved Scapula Segmentation in MR images

arXiv: https://arxiv.org/abs/1910.08963 · HTML: https://mlsanity.com/paper/1910.08963/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Contour Flow Constraint: Preserving Global Shape Similarity for Deep Learning based Image Segmentation](https://mlsanity.com/paper/2504.09384/) (2025)
- [Recursive 3D Segmentation of Shoulder Joint with Coarse-scanned MR Image](https://mlsanity.com/paper/2203.07846/) (2022)
- [Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation](https://mlsanity.com/paper/2409.13001/) (2024)

## Abstract (arXiv)

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.