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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Acknowledgment
This work is supported by grants from Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China (No. ZDSYS201605101739178), Natural Science Foundation of Guangdong (No. 2018A030313100), Research Grants Council of the Hong Kong Special Administrative Region (No. GRF 14225616), Guangdong Province Science and Technology Plan Project (No. 2016A020220013), the Science and Technology Plan Project of Guangzhou (No. 201704020141) and China Posdoctoral Science Foundation (No. 2017M622831).
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Li, C. et al. (2019). Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science(), vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_28
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DOI: https://doi.org/10.1007/978-3-030-12029-0_28
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