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Segmentation of the left atrial appendage based on fusion attention

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Abstract

In clinical practice, the morphology of the left atrial appendage (LAA) plays an important role in the selection of LAA closure devices for LAA closure procedures. The morphology determination is influenced by the segmentation results. The LAA occupies only a small part of the entire 3D medical image, and the segmentation results are more likely to be biased towards the background region, making the segmentation of the LAA challenging. In this paper, we propose a lightweight attention mechanism called fusion attention, which imitates human visual behavior. We process the 3D image of the LAA using a method that involves overview observation followed by detailed observation. In the overview observation stage, the image features are pooled along the three dimensions of length, width, and height. The obtained features from the three dimensions are then separately input into the spatial attention and channel attention modules to learn the regions of interest. In the detailed observation stage, the attention results from the previous stage are fused using element-wise multiplication and combined with the original feature map to enhance feature learning. The fusion attention mechanism was evaluated on a left atrial appendage dataset provided by Liaoning Provincial People’s Hospital, resulting in an average Dice coefficient of 0.8855. The results indicate that the fusion attention mechanism achieves better segmentation results on 3D images compared to existing lightweight attention mechanisms.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61971118, No. 61373088, No. 61402298), the National Natural Science Foundation of Liaoning (No. LJKMZ20220523), and the Aviation Science Foundation (2019ZE054009).

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Authors and Affiliations

  1. School of Computer Sciences, Shenyang Aerospace University, Shenyang, China

    Guodong Zhang, Kaichao Liang, Yanlin Li, Tingyu Liang & Zhaoxuan Gong

  2. Department of Radiology, People’s Hospital of Liaoning Province, Shenyang, China

    Ronghui Ju

  3. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China

    Guodong Zhang & Dazhe Zhao

  4. Luyuan Hydropower Company Maintenance Company, State Grid, Dandong, China

    Zhuoning Zhang

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  1. Guodong Zhang
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  2. Kaichao Liang
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  3. Yanlin Li
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Correspondence to Guodong Zhang or Ronghui Ju.

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The subject was reviewed by the Ethics Committee of the People’s Hospital of Liaoning Province, Grant No. (2022) H001.

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Zhang, G., Liang, K., Li, Y. et al. Segmentation of the left atrial appendage based on fusion attention. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03104-0

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