mlsanityData to 7 Oct 2026

An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation

arXiv 1709.04496 ↗First posted 13 Sep 2017cs.CVcited by 36

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Abstract (arXiv)

Accurate segmentation of the heart is an important step towards evaluating cardiac function. In this paper, we present a fully automated framework for segmentation of the left (LV) and right (RV) ventricular cavities and the myocardium (Myo) on short-axis cardiac MR images. We investigate various 2D and 3D convolutional neural network architectures for this task. We investigate the suitability of various state-of-the art 2D and 3D convolutional neural network architectures, as well as slight modifications thereof, for this task. Experiments were performed on the ACDC 2017 challenge training dataset comprising cardiac MR images of 100 patients, where manual reference segmentations were made available for end-diastolic (ED) and end-systolic (ES) frames. We find that processing the images in a slice-by-slice fashion using 2D networks is beneficial due to a relatively large slice thickness. However, the exact network architecture only plays a minor role. We report mean Dice coefficients of 0.950 (LV), 0.893 (RV), and 0.899 (Myo), respectively with an average evaluation time of 1.1 seconds per volume on a modern GPU.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2017.

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Citing papers per year: 2021: 12; 2022: 9; 2023: 3; 2024: 6; 2025: 3; 2026: 3.

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  1. 2022CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervisioncited by 64
  2. 2021Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentationcited by 43
  3. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  4. 2021More than Encoder: Introducing Transformer Decoder to Upsamplecited by 26
  5. 2022ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentationcited by 26
  6. 2024ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentationcited by 24
  7. 2023ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embeddingcited by 23
  8. 2021Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentationcited by 20
  9. 2021Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Diseasecited by 19
  10. 2022BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentationcited by 14
  11. 2021Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Datasetcited by 8
  12. 2023ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentationcited by 8
24 more
  1. 2021Region-wise Loss for Biomedical Image Segmentationcited by 7
  2. 2021Efficient Model Monitoring for Quality Control in Cardiac Image Segmentationcited by 6
  3. 2022Metric Based Few-Shot Graph Classificationcited by 4
  4. 2024ModelMix: A New Model-Mixup Strategy to Minimize Vicinal Risk across Tasks for Few-scribble based Cardiac Segmentationcited by 3
  5. 2024From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labelscited by 3
  6. 2021Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networkscited by 2
  7. 2021Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising Modelcited by 2
  8. 2021Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequencescited by 2
  9. 2022Deep Compatible Learning for Partially-Supervised Medical Image Segmentationcited by 2
  10. 2024How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluationcited by 2
  11. 2025AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flowscited by 2
  12. 2024Compositional Segmentation of Cardiac Images Leveraging Metadatacited by 1
  13. 2025Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learningcited by 1
  14. 2021Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networkscited by 0
  15. 2022Super Images -- A New 2D Perspective on 3D Medical Imaging Analysiscited by 0
  16. 2022Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Reviewcited by 0
  17. 2022Scoliosis Detection using Deep Neural Networkcited by 0
  18. 2022High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Losscited by 0
  19. 2023Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligencecited by 0
  20. 2024Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studiescited by 0
  21. 2025From Instance Segmentation to 3D Growth Trajectory Reconstruction in Planktonic Foraminiferacited by 0
  22. 2026ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervisioncited by 0
  23. 2026SV-Cine: Diagnosis-Conditioned Segmentation of Single Ventricle Physiology via Generative Data Augmentationcited by 0
  24. 2026Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouringcited by 0