mlsanityData to 7 Oct 2026

Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

arXiv 2108.04016 ↗First posted 9 Aug 2021eess.IV cs.CVcited by 28

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Abstract (arXiv)

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofold. First, to evaluate if deep learning methods can distinguish between normal and pathological cases. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 21 of its 56 references that are in the map. First posted on arXiv 9 Aug 2021.

Builds on

21 of 56 references.

References · 21

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9 more
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  5. 2020Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRIcited by 12
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 7; 2024: 5; 2025: 8; 2026: 4.

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  1. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  2. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  3. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  4. 2023The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challengecited by 25
  5. 2021Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentationcited by 22
  6. 2023SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularizationcited by 19
  7. 2022Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoderscited by 17
  8. 2024LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Modelscited by 15
  9. 2024Towards a vision foundation model for comprehensive assessment of Cardiac MRIcited by 13
  10. 2025SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Imagescited by 11
  11. 2023Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectificationcited by 8
  12. 2025MedSG-Bench: A Benchmark for Medical Image Sequences Groundingcited by 8
16 more
  1. 2025TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiencycited by 8
  2. 2021Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentationcited by 5
  3. 2023Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentationcited by 4
  4. 2024Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Modelscited by 4
  5. 2025Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labelscited by 3
  6. 2023CaRe-CNN: Cascading Refinement CNN for Myocardial Infarct Segmentation with Microvascular Obstructionscited by 2
  7. 2025Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imagingcited by 2
  8. 2025CLAIM: Clinically-Guided LGE Augmentation for Realistic and Diverse Myocardial Scar Synthesis and Segmentationcited by 2
  9. 2023Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance imagescited by 1
  10. 2025Fake It Till You Make It: Using Synthetic Data and Domain Knowledge for Improved Text-Based Learning for LGE Detectioncited by 1
  11. 2026Universality Reconsidered: Rethinking the Validation of Foundation Models for General-Purpose 3D Medical Segmentationcited by 1
  12. 2024Mind the Context: Attention-Guided Weak-to-Strong Consistency for Enhanced Semi-Supervised Medical Image Segmentationcited by 0
  13. 2025Deep Learning Pipeline for Fully Automated Myocardial Infarct Segmentation from Clinical Cardiac MR Scanscited by 0
  14. 2026LGESynthNet: Controlled Scar Synthesis for Improved Scar Segmentation in Cardiac LGE-MRI Imagingcited by 0
  15. 2026SemiSAM-O1: Pushing the Boundary of Annotation-Efficient Medical Image Segmentation with Generalist Knowledge Fusioncited by 0
  16. 2026ORION-CMR: On-scanner Reporting with Integrated Foundation Model for End-to-End Cardiac MRI Analysis and Interpretationcited by 0