# A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI

arXiv: https://arxiv.org/abs/1604.00494 · HTML: https://mlsanity.com/paper/1604.00494/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2016.

## Built on by (most cited first, 16 of 16)

- [DAM-Seg: Anatomically accurate cardiac segmentation using Dense Associative Networks](https://mlsanity.com/paper/2502.15128/) (2025)
- [GAEI-UNet: Global Attention and Elastic Interaction U-Net for Vessel Image Segmentation](https://mlsanity.com/paper/2308.08345/) (2023)
- [Two Deep Learning Approaches for Automated Segmentation of Left Ventricle in Cine Cardiac MRI](https://mlsanity.com/paper/2601.00794/) (2026)
- [Improving Image co-segmentation via Deep Metric Learning](https://mlsanity.com/paper/2103.10670/) (2021)
- [Individualized Risk Assessment of Preoperative Opioid Use by Interpretable Neural Network Regression](https://mlsanity.com/paper/2205.08370/) (2022)
- [Automated segmentation and morphological characterization of placental histology images based on a single labeled image](https://mlsanity.com/paper/2210.03566/) (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)
- [LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation](https://mlsanity.com/paper/2405.15779/) (2024)
- [RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation](https://mlsanity.com/paper/2409.05280/) (2024)
- [QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction](https://mlsanity.com/paper/2412.07156/) (2024)
- [Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation](https://mlsanity.com/paper/2503.17896/) (2025)
- [Convolutional Feature Noise Reduction for 2D Cardiac MR Image Segmentation](https://mlsanity.com/paper/2511.22983/) (2025)
- [Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains](https://mlsanity.com/paper/2512.13534/) (2025)
- [A Novel Deep Learning Method for Segmenting the Left Ventricle in Cardiac Cine MRI](https://mlsanity.com/paper/2601.01512/) (2026)
- [Contour-Guided Query-Based Feature Fusion for Boundary-Aware and Generalizable Cardiac Ultrasound Segmentation](https://mlsanity.com/paper/2603.28110/) (2026)

## Abstract (arXiv)

Automated cardiac segmentation from magnetic resonance imaging datasets is an essential step in the timely diagnosis and management of cardiac pathologies. We propose to tackle the problem of automated left and right ventricle segmentation through the application of a deep fully convolutional neural network architecture. Our model is efficiently trained end-to-end in a single learning stage from whole-image inputs and ground truths to make inference at every pixel. To our knowledge, this is the first application of a fully convolutional neural network architecture for pixel-wise labeling in cardiac magnetic resonance imaging. Numerical experiments demonstrate that our model is robust to outperform previous fully automated methods across multiple evaluation measures on a range of cardiac datasets. Moreover, our model is fast and can leverage commodity compute resources such as the graphics processing unit to enable state-of-the-art cardiac segmentation at massive scales. The models and code are available at https://github.com/vuptran/cardiac-segmentation