# Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network

arXiv: https://arxiv.org/abs/1704.05698 · HTML: https://mlsanity.com/paper/1704.05698/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2017.

## Built on by (most cited first, 4 of 4)

- [Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks](https://mlsanity.com/paper/2103.02969/) (2021)
- [Dispensed Transformer Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2110.14944/) (2021)
- [Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation](https://mlsanity.com/paper/2410.10551/) (2024)
- [Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation](https://mlsanity.com/paper/2301.08782/) (2023)

## Abstract (arXiv)

Accurate delineation of the left ventricle (LV) is an important step in evaluation of cardiac function. In this paper, we present an automatic method for segmentation of the LV in cardiac CT angiography (CCTA) scans. Segmentation is performed in two stages. First, a bounding box around the LV is detected using a combination of three convolutional neural networks (CNNs). Subsequently, to obtain the segmentation of the LV, voxel classification is performed within the defined bounding box using a CNN. The study included CCTA scans of sixty patients, fifty scans were used to train the CNNs for the LV localization, five scans were used to train LV segmentation and the remaining five scans were used for testing the method. Automatic segmentation resulted in the average Dice coefficient of 0.85 and mean absolute surface distance of 1.1 mm. The results demonstrate that automatic segmentation of the LV in CCTA scans using voxel classification with convolutional neural networks is feasible.