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2019 | OriginalPaper | Chapter

Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation

Authors : Caizi Li, Qianqian Tong, Xiangyun Liao, Weixin Si, Yinzi Sun, Qiong Wang, Pheng-Ann Heng

Published in: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges

Publisher: Springer International Publishing

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Abstract

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. The atrial segmentation is essential for the understanding of the human atria structure which is vital to the AF treatment. In this paper, we propose a novel three-dimensional (3D) segmentation network combining hierarchical aggregation and attention mechanism for 3D left atrial segmentation, named attention based hierarchical aggregation network (HAANet). In our network, the shallow and deep feature fusion capability of encoder-decoder convolutional neural networks is enhanced through hierarchical aggregation. Besides, attention mechanism is adopted to promote the ability of extracting efficient features. Experimental results demonstrate the HAANet can produce good results for 3D left atrial segmentation and the dice score of our HAANet reaches 92.30.

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previous chapter Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation

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Title: Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation
Authors: Caizi Li
Qianqian Tong
Xiangyun Liao
Weixin Si
Yinzi Sun
Qiong Wang
Pheng-Ann Heng
Publisher: Springer International Publishing
Book: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges
Print ISBN: 978-3-030-12028-3

Electronic ISBN: 978-3-030-12029-0

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-12029-0_28

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