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Multi-task Learning for Left Atrial Segmentation on GE-MRI

  • Chen ChenEmail author
  • Wenjia Bai
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11395)

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 https://github.com/cherise215/atria_segmentation_2018/.

Keywords

Multi-task learning Atrial segmentation Fully convolution neural network 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Biomedical Image Analysis GroupImperial College LondonLondonUK

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