# Multi-Structure Deep Segmentation with Shape Priors and Latent Adversarial Regularization

arXiv: https://arxiv.org/abs/2101.10173 · HTML: https://mlsanity.com/paper/2101.10173/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology](https://mlsanity.com/paper/1910.01877/) (2019)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network](https://mlsanity.com/paper/1809.01687/) (2018)
- [Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders](https://mlsanity.com/paper/1901.01620/) (2019)
- [Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network](https://mlsanity.com/paper/2009.07092/) (2020)

## Built on by (most cited first, 2 of 2)

- [Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors](https://mlsanity.com/paper/2207.13502/) (2022)
- [Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation](https://mlsanity.com/paper/2409.13001/) (2024)

## Abstract (arXiv)

Automatic segmentation of the musculoskeletal system in pediatric magnetic resonance (MR) images is a challenging but crucial task for morphological evaluation in clinical practice. We propose a deep learning-based regularized segmentation method for multi-structure bone delineation in MR images, designed to overcome the inherent scarcity and heterogeneity of pediatric data. Based on a newly devised shape code discriminator, our adversarial regularization scheme enforces the deep network to follow a learnt shape representation of the anatomy. The novel shape priors based adversarial regularization (SPAR) exploits latent shape codes arising from ground truth and predicted masks to guide the segmentation network towards more consistent and plausible predictions. Our contribution is compared to state-of-the-art regularization methods on two pediatric musculoskeletal imaging datasets from ankle and shoulder joints.