# Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images

arXiv: https://arxiv.org/abs/1708.01141 · HTML: https://mlsanity.com/paper/1708.01141/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2017.

## Built on by (most cited first, 13 of 13)

- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [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)
- [Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation](https://mlsanity.com/paper/2101.02375/) (2021)
- [3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks](https://mlsanity.com/paper/2307.07298/) (2023)
- [A Survey of Historical Learning: Learning Models with Learning History](https://mlsanity.com/paper/2303.12992/) (2023)
- [Multi-objective point cloud autoencoders for explainable myocardial infarction prediction](https://mlsanity.com/paper/2307.11017/) (2023)
- [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)
- [Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation](https://mlsanity.com/paper/2301.08782/) (2023)
- [Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning](https://mlsanity.com/paper/2405.14300/) (2024)
- [Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies](https://mlsanity.com/paper/2412.12853/) (2024)
- [IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classification](https://mlsanity.com/paper/2505.03838/) (2025)

## Abstract (arXiv)

Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images. A convolutional neural network (CNN) was designed to simultaneously segment the left ventricle (LV), right ventricle (RV) and myocardium in end-diastole (ED) and end-systole (ES) images. Features derived from the obtained segmentations were used in a Random Forest classifier to label patients as suffering from dilated cardiomyopathy, hypertrophic cardiomyopathy, heart failure following myocardial infarction, right ventricular abnormality, or no cardiac disease. The method was developed and evaluated using a balanced dataset containing images of 100 patients, which was provided in the MICCAI 2017 automated cardiac diagnosis challenge (ACDC). The segmentation and classification pipeline were evaluated in a four-fold stratified cross-validation. Average Dice scores between reference and automatically obtained segmentations were 0.94, 0.88 and 0.87 for the LV, RV and myocardium. The classifier assigned 91% of patients to the correct disease category. Segmentation and disease classification took 5 s per patient. The results of our study suggest that image-based diagnosis using cine MR cardiac scans can be performed automatically with high accuracy.