Multi-task Learning for Left Atrial Segmentation on GE-MRI
Chen C, Bai W, Rueckert D (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11395 LNCS
Pages Range: 292-301
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030120283
DOI: 10.1007/978-3-030-12029-0_32
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/.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Chen, C., Bai, W., & Rueckert, D. (2019). Multi-task Learning for Left Atrial Segmentation on GE-MRI. In Kristin McLeod, Alistair Young, Kawal Rhode, Shuo Li, Jichao Zhao, Tommaso Mansi, Maxime Sermesant, Mihaela Pop (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 292-301). Granada, ESP: Springer Verlag.
MLA:
Chen, Chen, Wenjia Bai, and Daniel Rueckert. "Multi-task Learning for Left Atrial Segmentation on GE-MRI." Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Kristin McLeod, Alistair Young, Kawal Rhode, Shuo Li, Jichao Zhao, Tommaso Mansi, Maxime Sermesant, Mihaela Pop, Springer Verlag, 2019. 292-301.
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