# Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation

arXiv: https://arxiv.org/abs/2302.03537 · HTML: https://mlsanity.com/paper/2302.03537/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Convolutional neural network architecture for geometric matching](https://mlsanity.com/paper/1703.05593/) (2017)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration](https://mlsanity.com/paper/1807.03361/) (2018)
- [A review: Deep learning for medical image segmentation using multi-modality fusion](https://mlsanity.com/paper/2004.10664/) (2020)
- [HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation](https://mlsanity.com/paper/1804.02967/) (2018)
- [Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences](https://mlsanity.com/paper/1806.04066/) (2018)
- [MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.03186/) (2022)
- [Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors](https://mlsanity.com/paper/1906.07347/) (2019)
- [Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review](https://mlsanity.com/paper/2106.09862/) (2021)
- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation](https://mlsanity.com/paper/2006.15573/) (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)
- [Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition](https://mlsanity.com/paper/2109.02171/) (2021)
- [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, 7 of 7)

- [Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference](https://mlsanity.com/paper/2307.04421/) (2023)
- [CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR](https://mlsanity.com/paper/2507.02289/) (2025)
- [Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN](https://mlsanity.com/paper/2409.12792/) (2024)
- [Learning Semi-Supervised Medical Image Segmentation from Spatial Registration](https://mlsanity.com/paper/2409.10422/) (2024)
- [MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generation](https://mlsanity.com/paper/2511.13135/) (2025)
- [CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations](https://mlsanity.com/paper/2606.00123/) (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) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable information. For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively. Existing methods usually fuse anatomical and pathological information from different CMR sequences for MyoPS, but assume that these images have been spatially aligned. However, MS-CMR images are usually unaligned due to the respiratory motions in clinical practices, which poses additional challenges for MyoPS. This work presents an automatic MyoPS framework for unaligned MS-CMR images. Specifically, we design a combined computing model for simultaneous image registration and information fusion, which aggregates multi-sequence features into a common space to extract anatomical structures (i.e., myocardium). Consequently, we can highlight the informative regions in the common space via the extracted myocardium to improve MyoPS performance, considering the spatial relationship between myocardial pathologies and myocardium. Experiments on a private MS-CMR dataset and a public dataset from the MYOPS2020 challenge show that our framework could achieve promising performance for fully automatic MyoPS.