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

arXiv: https://arxiv.org/abs/1709.04496 · HTML: https://mlsanity.com/paper/1709.04496/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 30 of 36)

- [CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision](https://mlsanity.com/paper/2203.01475/) (2022)
- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [More than Encoder: Introducing Transformer Decoder to Upsample](https://mlsanity.com/paper/2106.10637/) (2021)
- [ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation](https://mlsanity.com/paper/2206.02118/) (2022)
- [ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation](https://mlsanity.com/paper/2402.02029/) (2024)
- [ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding](https://mlsanity.com/paper/2307.16226/) (2023)
- [Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation](https://mlsanity.com/paper/2108.03429/) (2021)
- [Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease](https://mlsanity.com/paper/2107.03901/) (2021)
- [BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2206.14409/) (2022)
- [Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset](https://mlsanity.com/paper/2103.08590/) (2021)
- [ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation](https://mlsanity.com/paper/2301.04882/) (2023)
- [Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation](https://mlsanity.com/paper/2104.05533/) (2021)
- [Region-wise Loss for Biomedical Image Segmentation](https://mlsanity.com/paper/2108.01405/) (2021)
- [Metric Based Few-Shot Graph Classification](https://mlsanity.com/paper/2206.03695/) (2022)
- [ModelMix: A New Model-Mixup Strategy to Minimize Vicinal Risk across Tasks for Few-scribble based Cardiac Segmentation](https://mlsanity.com/paper/2406.13237/) (2024)
- [From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels](https://mlsanity.com/paper/2408.12814/) (2024)
- [Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks](https://mlsanity.com/paper/2101.07653/) (2021)
- [Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising Model](https://mlsanity.com/paper/2104.13082/) (2021)
- [Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences](https://mlsanity.com/paper/2106.12153/) (2021)
- [Deep Compatible Learning for Partially-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2206.09148/) (2022)
- [How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation](https://mlsanity.com/paper/2408.06358/) (2024)
- [AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows](https://mlsanity.com/paper/2503.12515/) (2025)
- [Compositional Segmentation of Cardiac Images Leveraging Metadata](https://mlsanity.com/paper/2410.23130/) (2024)
- [Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning](https://mlsanity.com/paper/2504.13391/) (2025)
- [Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks](https://mlsanity.com/paper/2108.01941/) (2021)
- [Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis](https://mlsanity.com/paper/2205.02847/) (2022)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Scoliosis Detection using Deep Neural Network](https://mlsanity.com/paper/2210.17269/) (2022)
- [High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss](https://mlsanity.com/paper/2211.02419/) (2022)

## 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.