# MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

arXiv: https://arxiv.org/abs/2201.03186 · HTML: https://mlsanity.com/paper/2201.03186/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 74 references that are in the map.
- First posted on arXiv 10 Jan 2022.

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

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions](https://mlsanity.com/paper/1909.00166/) (2019)
- [Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge](https://mlsanity.com/paper/2006.12434/) (2020)
- [Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review](https://mlsanity.com/paper/2106.09862/) (2021)
- [Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences](https://mlsanity.com/paper/2008.07770/) (2020)
- [Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2008.05780/) (2020)
- [Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling](https://mlsanity.com/paper/2009.02569/) (2020)

## Built on by (most cited first, 29 of 29)

- [MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images](https://mlsanity.com/paper/2211.03062/) (2022)
- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference](https://mlsanity.com/paper/2307.04421/) (2023)
- [PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation](https://mlsanity.com/paper/2208.09350/) (2022)
- [Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey](https://mlsanity.com/paper/2406.11445/) (2024)
- [A versatile foundation model for cine cardiac magnetic resonance image analysis tasks](https://mlsanity.com/paper/2506.00679/) (2025)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Learning correspondences of cardiac motion from images using biomechanics-informed modeling](https://mlsanity.com/paper/2209.00726/) (2022)
- [ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation](https://mlsanity.com/paper/2301.04882/) (2023)
- [Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation](https://mlsanity.com/paper/2302.03537/) (2023)
- [Progressive Test Time Energy Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2503.16616/) (2025)
- [3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks](https://mlsanity.com/paper/2307.07298/) (2023)
- [Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion](https://mlsanity.com/paper/2501.05241/) (2025)
- [ModelMix: A New Model-Mixup Strategy to Minimize Vicinal Risk across Tasks for Few-scribble based Cardiac Segmentation](https://mlsanity.com/paper/2406.13237/) (2024)
- [CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR](https://mlsanity.com/paper/2507.02289/) (2025)
- [Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging](https://mlsanity.com/paper/2503.19005/) (2025)
- [CLAIM: Clinically-Guided LGE Augmentation for Realistic and Diverse Myocardial Scar Synthesis and Segmentation](https://mlsanity.com/paper/2506.15549/) (2025)
- [Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images](https://mlsanity.com/paper/2306.14725/) (2023)
- [Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN](https://mlsanity.com/paper/2409.12792/) (2024)
- [MedCL: Learning Consistent Anatomy Distribution for Scribble-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.22890/) (2025)
- [Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge](https://mlsanity.com/paper/2507.19165/) (2025)
- [Seeing Beyond the Image: ECG and Anatomical Knowledge-Guided Myocardial Scar Segmentation from Late Gadolinium-Enhanced Images](https://mlsanity.com/paper/2511.14702/) (2025)
- [Deep Learning Pipeline for Fully Automated Myocardial Infarct Segmentation from Clinical Cardiac MR Scans](https://mlsanity.com/paper/2502.03272/) (2025)
- [MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generation](https://mlsanity.com/paper/2511.13135/) (2025)
- [SynerMedGen: Synergizing Medical Multimodal Understanding with Generation via Task Alignment](https://mlsanity.com/paper/2605.08724/) (2026)
- [Vision Transformer-Conditioned UNet for Domain-Adaptive Semantic Segmentation](https://mlsanity.com/paper/2605.16393/) (2026)
- [CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations](https://mlsanity.com/paper/2606.00123/) (2026)
- [The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification](https://mlsanity.com/paper/2608.29246/) (2026)

## Abstract (arXiv)

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).