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

arXiv 2201.03186 ↗First posted 10 Jan 2022eess.IV cs.CVcited by 29

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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/).

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 13 of its 74 references that are in the map. First posted on arXiv 10 Jan 2022.

Builds on

13 of 74 references.

References · 13

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  9. 2020Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challengecited by 41
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1 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 6; 2024: 3; 2025: 11; 2026: 4.

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  1. 2022MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Imagescited by 22
  2. 2022Multi-Modality Cardiac Image Computing: A Surveycited by 19
  3. 2023Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inferencecited by 18
  4. 2022PyMIC: A deep learning toolkit for annotation-efficient medical image segmentationcited by 14
  5. 2024Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Surveycited by 14
  6. 2025A versatile foundation model for cine cardiac magnetic resonance image analysis taskscited by 12
  7. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  8. 2023Attention Mechanisms in Medical Image Segmentation: A Surveycited by 9
  9. 2022Learning correspondences of cardiac motion from images using biomechanics-informed modelingcited by 8
  10. 2023ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentationcited by 8
  11. 2023Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentationcited by 7
  12. 2025Progressive Test Time Energy Adaptation for Medical Image Segmentationcited by 5
17 more
  1. 20233D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networkscited by 4
  2. 2025Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusioncited by 4
  3. 2024ModelMix: A New Model-Mixup Strategy to Minimize Vicinal Risk across Tasks for Few-scribble based Cardiac Segmentationcited by 3
  4. 2025CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MRcited by 3
  5. 2025Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imagingcited by 2
  6. 2025CLAIM: Clinically-Guided LGE Augmentation for Realistic and Diverse Myocardial Scar Synthesis and Segmentationcited by 2
  7. 2023Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance imagescited by 1
  8. 2024Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNNcited by 1
  9. 2025MedCL: Learning Consistent Anatomy Distribution for Scribble-supervised Medical Image Segmentationcited by 1
  10. 2025Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challengecited by 1
  11. 2025Seeing Beyond the Image: ECG and Anatomical Knowledge-Guided Myocardial Scar Segmentation from Late Gadolinium-Enhanced Imagescited by 1
  12. 2025Deep Learning Pipeline for Fully Automated Myocardial Infarct Segmentation from Clinical Cardiac MR Scanscited by 0
  13. 2025MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generationcited by 0
  14. 2026SynerMedGen: Synergizing Medical Multimodal Understanding with Generation via Task Alignmentcited by 0
  15. 2026Vision Transformer-Conditioned UNet for Domain-Adaptive Semantic Segmentationcited by 0
  16. 2026CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluationscited by 0
  17. 2026The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantificationcited by 0