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

Abstract

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 $0.849\pm 0.036$ and mean surface distance of $0.274\pm 0.083$ mm, while simultaneously estimating the myocardial area with mean absolute difference error of $205\pm 198$ mm${}^2$.

STACOM 2018 Workshop, MICCAI 2018

References in corpus (5)

• A Survey on Deep Learning in Medical Image Analysis
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• A Review on Deep Learning Techniques Applied to Semantic Segmentation
• Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
• Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning

Cited by in corpus (2)

• Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning
• TAN: Temporal Affine Network for Real-Time Left Ventricle Anatomical Structure Analysis Based on 2D Ultrasound Videos