# Cardiac MRI Segmentation with Strong Anatomical Guarantees

arXiv: https://arxiv.org/abs/1907.02865 · HTML: https://mlsanity.com/paper/1907.02865/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 14 of 14)

- [Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder](https://mlsanity.com/paper/2105.00026/) (2021)
- [A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR](https://mlsanity.com/paper/2107.12689/) (2021)
- [AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.05344/) (2022)
- [Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation](https://mlsanity.com/paper/2107.01079/) (2021)
- [Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts](https://mlsanity.com/paper/2108.11926/) (2021)
- [Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training](https://mlsanity.com/paper/2108.12280/) (2021)
- [Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views](https://mlsanity.com/paper/2109.11715/) (2021)
- [Deep Statistic Shape Model for Myocardium Segmentation](https://mlsanity.com/paper/2207.10607/) (2022)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)
- [Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images](https://mlsanity.com/paper/2112.03455/) (2021)
- [Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views](https://mlsanity.com/paper/2309.12805/) (2023)
- [Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors](https://mlsanity.com/paper/2310.08084/) (2023)
- [Scoliosis Detection using Deep Neural Network](https://mlsanity.com/paper/2210.17269/) (2022)
- [ConvFormer3D-TAP: Phase/Uncertainty-Aware Front-End Fusion for Cine CMR View Classification Pipelines](https://mlsanity.com/paper/2604.11389/) (2026)

## Abstract (arXiv)

Recent publications have shown that the segmentation accuracy of modern-day convolutional neural networks (CNN) applied on cardiac MRI can reach the inter-expert variability, a great achievement in this area of research. However, despite these successes, CNNs still produce anatomically inaccurate segmentations as they provide no guarantee on the anatomical plausibility of their outcome, even when using a shape prior. In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results. At the core of the method is an adversarial variational autoencoder (aVAE) whose latent space encodes a smooth manifold on which lies a large spectrum of valid cardiac shapes. This aVAE is used to automatically warp anatomically inaccurate cardiac shapes towards a close but correct shape. Our method can accommodate any cardiac segmentation method and convert its anatomically implausible results to plausible ones without affecting its overall geometric and clinical metrics. With our method, CNNs can now produce results that are both within the inter-expert variability and always anatomically plausible.