# Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation

arXiv: https://arxiv.org/abs/2410.10551 · HTML: https://mlsanity.com/paper/2410.10551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability](https://mlsanity.com/paper/2303.01710/) (2023)
- [Learning Topological Interactions for Multi-Class Medical Image Segmentation](https://mlsanity.com/paper/2207.09654/) (2022)
- [ConvNet-Based Localization of Anatomical Structures in 3D Medical Images](https://mlsanity.com/paper/1704.05629/) (2017)
- [A two-stage 3D Unet framework for multi-class segmentation on full resolution image](https://mlsanity.com/paper/1804.04341/) (2018)
- [CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation](https://mlsanity.com/paper/1812.04914/) (2018)
- [CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN](https://mlsanity.com/paper/1705.06333/) (2017)
- [Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network](https://mlsanity.com/paper/1704.05698/) (2017)

## Built on by (most cited first, 1 of 1)

- [Relational Anatomical Supervision for Accurate 3D Multi-Chamber Cardiac Mesh Reconstruction](https://mlsanity.com/paper/2503.07874/) (2025)

## Abstract (arXiv)

Whole heart segmentation (WHS) supports cardiovascular disease (CVD) diagnosis, disease monitoring, treatment planning, and prognosis. Deep learning has become the most widely used method for WHS applications in recent years. However, segmentation of whole-heart structures faces numerous challenges including heart shape variability during the cardiac cycle, clinical artifacts like motion and poor contrast-to-noise ratio, domain shifts in multi-center data, and the distinct modalities of CT and MRI. To address these limitations and improve segmentation quality, this paper introduces a new topology-preserving module that is integrated into deep neural networks. The implementation achieves anatomically plausible segmentation by using learned topology-preserving fields, which are based entirely on 3D convolution and are therefore very effective for 3D voxel data. We incorporate natural constraints between structures into the end-to-end training and enrich the feature representation of the neural network. The effectiveness of the proposed method is validated on an open-source medical heart dataset, specifically using the WHS++ data. The results demonstrate that the architecture performs exceptionally well, achieving a Dice coefficient of 0.939 during testing. This indicates full topology preservation for individual structures and significantly outperforms other baselines in preserving the overall scene topology.