# Cardiac Segmentation with Strong Anatomical Guarantees

arXiv: https://arxiv.org/abs/2006.08825 · HTML: https://mlsanity.com/paper/2006.08825/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 30 of 44)

- [Echocardiography Segmentation with Enforced Temporal Consistency](https://mlsanity.com/paper/2112.02102/) (2021)
- [GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation](https://mlsanity.com/paper/2207.02466/) (2022)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR](https://mlsanity.com/paper/2107.12689/) (2021)
- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2206.14409/) (2022)
- [Anatomically-aware Uncertainty for Semi-supervised Image Segmentation](https://mlsanity.com/paper/2310.16099/) (2023)
- [Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation](https://mlsanity.com/paper/2107.03887/) (2021)
- [CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation](https://mlsanity.com/paper/2206.07664/) (2022)
- [Shape of my heart: Cardiac models through learned signed distance functions](https://mlsanity.com/paper/2308.16568/) (2023)
- [Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation](https://mlsanity.com/paper/2104.05533/) (2021)
- [Learning With Context Feedback Loop for Robust Medical Image Segmentation](https://mlsanity.com/paper/2103.02844/) (2021)
- [Generative Sampling in Bundle Tractography using Autoencoders (GESTA)](https://mlsanity.com/paper/2204.10891/) (2022)
- [Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification](https://mlsanity.com/paper/2206.13123/) (2022)
- [An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.07032/) (2024)
- [Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging](https://mlsanity.com/paper/2406.04100/) (2024)
- [FIESTA: Autoencoders for accurate fiber segmentation in tractography](https://mlsanity.com/paper/2212.00143/) (2022)
- [Shape-intensity knowledge distillation for robust medical image segmentation](https://mlsanity.com/paper/2409.17503/) (2024)
- [Deep Statistic Shape Model for Myocardium Segmentation](https://mlsanity.com/paper/2207.10607/) (2022)
- [Towards Robust Cardiac Segmentation using Graph Convolutional Networks](https://mlsanity.com/paper/2310.01210/) (2023)
- [Deep Conditional Shape Models for 3D cardiac image segmentation](https://mlsanity.com/paper/2310.10756/) (2023)
- [Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification](https://mlsanity.com/paper/2401.07796/) (2024)
- [DAM-Seg: Anatomically accurate cardiac segmentation using Dense Associative Networks](https://mlsanity.com/paper/2502.15128/) (2025)
- [Generative augmentations for improved cardiac ultrasound segmentation using diffusion models](https://mlsanity.com/paper/2502.20100/) (2025)
- [Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation](https://mlsanity.com/paper/2106.08188/) (2021)
- [Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning](https://mlsanity.com/paper/2406.17902/) (2024)
- [How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation](https://mlsanity.com/paper/2408.06358/) (2024)
- [Train-Free Segmentation in MRI with Cubical Persistent Homology](https://mlsanity.com/paper/2401.01160/) (2024)
- [An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion](https://mlsanity.com/paper/2410.06818/) (2024)
- [Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning](https://mlsanity.com/paper/2504.13391/) (2025)

## Abstract (arXiv)

Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation results are closer than ever to the inter-expert variability, CNNs are not immune to producing anatomically inaccurate segmentations, even when built upon a shape prior. In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability. The idea behind our method is to use a well-trained CNN, have it process cardiac images, identify the anatomically implausible results and warp these results toward the closest anatomically valid cardiac shape. This warping procedure is carried out with a constrained variational autoencoder (cVAE) trained to learn a representation of valid cardiac shapes through a smooth, yet constrained, latent space. With this cVAE, we can project any implausible shape into the cardiac latent space and steer it toward the closest correct shape. We tested our framework on short-axis MRI as well as apical two and four-chamber view ultrasound images, two modalities for which cardiac shapes are drastically different. With our method, CNNs can now produce results that are both within the inter-expert variability and always anatomically plausible without having to rely on a shape prior.