Multi-task learning for left atrial segmentation on GE-MRI

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Title: Multi-task learning for left atrial segmentation on GE-MRI
Authors: Chen, C
Bai, W
Rueckert, D
Item Type: Conference Paper
Abstract: Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies. In this paper, we present a fully automated framework for left atrial segmentation in gadolinium-enhanced magnetic resonance images (GE-MRI) based on deep learning. We propose a fully convolutional neural network and explore the benefits of multi-task learning for performing both atrial segmentation and pre/post ablation classification. Our results show that, by sharing features between related tasks, the network can gain additional anatomical information and achieve more accurate atrial segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI images. Code of our proposed algorithm is available at
Issue Date: 14-Feb-2019
Date of Acceptance: 1-Sep-2018
ISSN: 0302-9743
Publisher: Springer Verlag
Start Page: 292
End Page: 301
Volume: 11395
Copyright Statement: © Springer Nature Switzerland AG 2019. The final publication is available at Springer via
Conference Name: International Workshop on Statistical Atlases and Computational Models of the Heart
Keywords: cs.CV
08 Information and Computing Sciences
Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2018-09-16
Conference Place: Granada, Spain
Online Publication Date: 2019-02-14
Appears in Collections:Faculty of Engineering
Department of Medicine
Faculty of Medicine

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