# Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI

arXiv: https://arxiv.org/abs/2011.07592 · HTML: https://mlsanity.com/paper/2011.07592/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 28 of 28)

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- [Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs](https://mlsanity.com/paper/2209.04223/) (2022)
- [Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation](https://mlsanity.com/paper/2203.00355/) (2022)
- [DB-SAM: Delving into High Quality Universal Medical Image Segmentation](https://mlsanity.com/paper/2410.04172/) (2024)
- [Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images](https://mlsanity.com/paper/2110.00682/) (2021)
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- [Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation](https://mlsanity.com/paper/2305.19084/) (2023)
- [The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images](https://mlsanity.com/paper/2109.13230/) (2021)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)
- [Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans](https://mlsanity.com/paper/2309.13747/) (2023)
- [How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation](https://mlsanity.com/paper/2408.06358/) (2024)
- [Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?](https://mlsanity.com/paper/2508.06327/) (2025)
- [Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.12805/) (2022)
- [ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation](https://mlsanity.com/paper/2312.05407/) (2023)
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- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [A quality assurance framework for real-time monitoring of deep learning segmentation models in radiotherapy](https://mlsanity.com/paper/2305.11715/) (2023)
- [SFB-net for cardiac segmentation: Bridging the semantic gap with attention](https://mlsanity.com/paper/2410.18503/) (2024)
- [Uncertainty-Based Ensemble Learning in CMR Semantic Segmentation](https://mlsanity.com/paper/2502.09269/) (2025)
- [Training deep learning based dynamic MR image reconstruction using synthetic fractals](https://mlsanity.com/paper/2603.29922/) (2026)
- [Towards 3D heart mesh generation using contactless radar imaging and physics-informed neural network](https://mlsanity.com/paper/2605.26003/) (2026)

## Abstract (arXiv)

Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases. Recently, deep neural networks have demonstrated successful automatic segmentation, thus alleviating the burden of time-consuming manual contouring of cardiac structures. Moreover, frameworks such as nnU-Net provide entirely automatic model configuration to unseen datasets enabling out-of-the-box application even by non-experts. However, current studies commonly neglect the clinically realistic scenario, in which a trained network is applied to data from a different domain such as deviating scanners or imaging protocols. This potentially leads to unexpected performance drops of deep learning models in real life applications. In this work, we systematically study challenges and opportunities of domain transfer across images from multiple clinical centres and scanner vendors. In order to maintain out-of-the-box usability, we build upon a fixed U-Net architecture configured by the nnU-net framework to investigate various data augmentation techniques and batch normalization layers as an easy-to-customize pipeline component and provide general guidelines on how to improve domain generalizability abilities in existing deep learning methods. Our proposed method ranked first at the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms).