mlsanityData to 7 Oct 2026

Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation

arXiv 2302.03537 ↗First posted 7 Feb 2023eess.IV cs.CVcited by 7

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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.

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

Builds on

19 of 58 references.

References · 19

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  12. 2022MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Imagescited by 29
7 more
  1. 2019Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priorscited by 27
  2. 2021Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Reviewcited by 26
  3. 2022Multi-Modality Cardiac Image Computing: A Surveycited by 19
  4. 2020MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimationcited by 10
  5. 2022AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Imagescited by 10
  6. 2021Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transitioncited by 9
  7. 2020Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resamplingcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 2.

7 papers.

  1. 2023Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inferencecited by 18
  2. 2025CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MRcited by 3
  3. 2024Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNNcited by 1
  4. 2024Learning Semi-Supervised Medical Image Segmentation from Spatial Registrationcited by 0
  5. 2025MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generationcited by 0
  6. 2026CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluationscited by 0
  7. 2026Learning Where to Look: Anatomical Grounding and Guided Attention for Cardiac MRI Vision-Language Modelscited by 0