# MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images

arXiv: https://arxiv.org/abs/2211.03062 · HTML: https://mlsanity.com/paper/2211.03062/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 58 references that are in the map.
- First posted on arXiv 6 Nov 2022.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [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)
- [A review: Deep learning for medical image segmentation using multi-modality fusion](https://mlsanity.com/paper/2004.10664/) (2020)
- [Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion](https://mlsanity.com/paper/2002.09708/) (2020)
- [HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation](https://mlsanity.com/paper/1804.02967/) (2018)
- [The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1704.01664/) (2017)
- [Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge](https://mlsanity.com/paper/2006.12434/) (2020)
- [A deep learning model integrating FCNNs and CRFs for brain tumor segmentation](https://mlsanity.com/paper/1702.04528/) (2017)
- [MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.03186/) (2022)
- [Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey](https://mlsanity.com/paper/2001.04074/) (2020)
- [AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information](https://mlsanity.com/paper/2008.04729/) (2020)
- [Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework](https://mlsanity.com/paper/1902.07877/) (2019)
- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention](https://mlsanity.com/paper/2006.13011/) (2020)
- [AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.05344/) (2022)
- [Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2008.05780/) (2020)

## Built on by (most cited first, 22 of 22)

- [Large-Vocabulary Segmentation for Medical Images with Text Prompts](https://mlsanity.com/paper/2312.17183/) (2023)
- [SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks](https://mlsanity.com/paper/2311.11969/) (2023)
- [Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2411.12814/) (2024)
- [Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference](https://mlsanity.com/paper/2307.04421/) (2023)
- [PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates](https://mlsanity.com/paper/2407.14796/) (2024)
- [Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation](https://mlsanity.com/paper/2508.20909/) (2025)
- [CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR](https://mlsanity.com/paper/2507.02289/) (2025)
- [Personalized Topology-Informed Localization of Standard 12-Lead ECG Electrode Placement from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins](https://mlsanity.com/paper/2408.13945/) (2024)
- [Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN](https://mlsanity.com/paper/2409.12792/) (2024)
- [An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion](https://mlsanity.com/paper/2410.06818/) (2024)
- [KA2ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation](https://mlsanity.com/paper/2410.21085/) (2024)
- [MedCL: Learning Consistent Anatomy Distribution for Scribble-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.22890/) (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)
- [Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks](https://mlsanity.com/paper/2308.00615/) (2023)
- [Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation](https://mlsanity.com/paper/2311.09737/) (2023)
- [DMAF-Net: An Effective Modality Rebalancing Framework for Incomplete Multi-Modal Medical Image Segmentation](https://mlsanity.com/paper/2506.11691/) (2025)
- [A Robust Incomplete Multimodal Low-Rank Adaptation Approach for Emotion Recognition](https://mlsanity.com/paper/2507.11202/) (2025)
- [MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generation](https://mlsanity.com/paper/2511.13135/) (2025)
- [BMDS-Net:Deployment-aware multi-modal brain tumor segmentation with adaptive fusion,decoder regularization,and Bayesian calibration](https://mlsanity.com/paper/2601.17504/) (2026)
- [CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations](https://mlsanity.com/paper/2606.00123/) (2026)
- [CalcSeg: Confidence-aware 3D Latent Context Curriculum Learning For Myocardial Scar Segmentation From Single-Stack LGE-CMRs](https://mlsanity.com/paper/2608.20305/) (2026)
- [Learning Where to Look: Anatomical Grounding and Guided Attention for Cardiac MRI Vision-Language Models](https://mlsanity.com/paper/2609.39899/) (2026)

## Abstract (arXiv)

Myocardial pathology segmentation (MyoPS) can be a prerequisite for the accurate diagnosis and treatment planning of myocardial infarction. However, achieving this segmentation is challenging, mainly due to the inadequate and indistinct information from an image. In this work, we develop an end-to-end deep neural network, referred to as MyoPS-Net, to flexibly combine five-sequence cardiac magnetic resonance (CMR) images for MyoPS. To extract precise and adequate information, we design an effective yet flexible architecture to extract and fuse cross-modal features. This architecture can tackle different numbers of CMR images and complex combinations of modalities, with output branches targeting specific pathologies. To impose anatomical knowledge on the segmentation results, we first propose a module to regularize myocardium consistency and localize the pathologies, and then introduce an inclusiveness loss to utilize relations between myocardial scars and edema. We evaluated the proposed MyoPS-Net on two datasets, i.e., a private one consisting of 50 paired multi-sequence CMR images and a public one from MICCAI2020 MyoPS Challenge. Experimental results showed that MyoPS-Net could achieve state-of-the-art performance in various scenarios. Note that in practical clinics, the subjects may not have full sequences, such as missing LGE CMR or mapping CMR scans. We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences. Results proved the superiority and generalizability of MyoPS-Net, and more importantly, indicated a practical clinical application.