# Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images

arXiv: https://arxiv.org/abs/2201.12805 · HTML: https://mlsanity.com/paper/2201.12805/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 30 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI](https://mlsanity.com/paper/2011.07592/) (2020)
- [Disentangled Representations for Domain-generalized Cardiac Segmentation](https://mlsanity.com/paper/2008.11514/) (2020)
- [Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information](https://mlsanity.com/paper/2008.12205/) (2020)
- [Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation](https://mlsanity.com/paper/2012.13871/) (2020)
- [Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer](https://mlsanity.com/paper/2012.14785/) (2020)
- [Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation](https://mlsanity.com/paper/2008.11776/) (2020)
- [Estimation of the volume of the left ventricle from MRI images using deep neural networks](https://mlsanity.com/paper/1702.03833/) (2017)
- [Style-invariant Cardiac Image Segmentation with Test-time Augmentation](https://mlsanity.com/paper/2009.12193/) (2020)
- [Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation](https://mlsanity.com/paper/2008.01216/) (2020)

## Built on by (most cited first, 1 of 1)

- [An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion](https://mlsanity.com/paper/2410.06818/) (2024)

## Abstract (arXiv)

Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction. The ejection fraction is a measurement that expresses the percentage of blood leaving the heart with each contraction. Cardiologists often use ejection fraction to determine one's cardiac function. We propose multiscale template matching technique for detection and an elliptical active disc for automated segmentation of the left ventricle in MR images. The elliptical active disc optimizes the local energy function with respect to its five free parameters which define the disc. Gradient descent is used to minimize the energy function along with Green's theorem to optimize the computation expenses. We report validations on 320 scans containing 5,273 annotated slices which are publicly available through the Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Segmentation (M&Ms) Challenge. We achieved successful localization of the left ventricle in 89.63% of the cases and a Dice coefficient of 0.873 on diastole slices and 0.770 on systole slices. The proposed technique is based on traditional image processing techniques with a performance on par with the deep learning techniques.