# Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation

arXiv: https://arxiv.org/abs/2012.13871 · HTML: https://mlsanity.com/paper/2012.13871/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs](https://mlsanity.com/paper/2106.08727/) (2021)
- [Online Reflective Learning for Robust Medical Image Segmentation](https://mlsanity.com/paper/2207.00476/) (2022)
- [Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?](https://mlsanity.com/paper/2101.00232/) (2021)
- [Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation](https://mlsanity.com/paper/2305.19084/) (2023)
- [How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation](https://mlsanity.com/paper/2408.06358/) (2024)
- [Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.12805/) (2022)
- [A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology](https://mlsanity.com/paper/2209.13408/) (2022)
- [Unsupervised Domain Adaptation with Histogram-gated Image Translation for Delayered IC Image Analysis](https://mlsanity.com/paper/2209.13479/) (2022)
- [Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI using Histogram Matching](https://mlsanity.com/paper/2302.11200/) (2023)
- [SFB-net for cardiac segmentation: Bridging the semantic gap with attention](https://mlsanity.com/paper/2410.18503/) (2024)
- [Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2512.01510/) (2025)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) have achieved high accuracy for cardiac structure segmentation if training cases and testing cases are from the same distribution. However, the performance would be degraded if the testing cases are from a distinct domain (e.g., new MRI scanners, clinical centers). In this paper, we propose a histogram matching (HM) data augmentation method to eliminate the domain gap. Specifically, our method generates new training cases by using HM to transfer the intensity distribution of testing cases to existing training cases. The proposed method is quite simple and can be used in a plug-and-play way in many segmentation tasks. The method is evaluated on MICCAI 2020 M&Ms challenge, and achieves average Dice scores of 0.9051, 0.8405, and 0.8749, and Hausdorff Distances of 9.996, 12.49, and 12.68 for the left ventricular, myocardium, and right ventricular, respectively. Our results rank the third place in MICCAI 2020 M&Ms challenge. The code and trained models are publicly available at urlhttps://github.com/JunMa11/HMDataAug.