# Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning

arXiv: https://arxiv.org/abs/1809.10221 · HTML: https://mlsanity.com/paper/1809.10221/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors](https://mlsanity.com/paper/2105.09670/) (2021)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)
- [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 and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled independently. Here we propose a convolutional neural network based multi-task learning approach to perform both tasks simultaneously, such that, the network learns better representation of the data with improved generalization performance. Probabilistic formulation of the problem enables learning the task uncertainties during the training, which are used to automatically compute the weights for the tasks. We performed a five fold cross-validation of the myocardium segmentation obtained from the proposed multi-task network on 97 patient 4-dimensional cardiac cine-MRI datasets available through the STACOM LV segmentation challenge against the provided gold-standard myocardium segmentation, obtaining a Dice overlap of 0.849 ± 0.036 and mean surface distance of 0.274 ± 0.083 mm, while simultaneously estimating the myocardial area with mean absolute difference error of 205±198 mm2.