A deep-learning-based geographic attention model for body composition tissue segmentation

Jian Dai; Jayaram K. Udupa; Drew A. Torigian; Yubing Tong; Pengju Nie; Jing Zhang; Ran Li; Shiwei Han; Tiange Liu

doi:10.1117/12.2653371

10 April 2023 A deep-learning-based geographic attention model for body composition tissue segmentation

Jian Dai, Jayaram K. Udupa, Drew A. Torigian, Yubing Tong, Pengju Nie, Jing Zhang, Ran Li, Shiwei Han, Tiange Liu

Proceedings Volume 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1246804 (2023) https://doi.org/10.1117/12.2653371
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

This paper proposes a deep neural network, Geographic Attention Model (GA-Net), for body composition tissue segmentation. By adding an auxiliary body area prediction task, our method exploits the rich semantic and spatial features contained in the body area and incorporates the features of both area and body composition tissue. In this way, GA-Net achieves superior performance for body composition tissue segmentation, especially for the indistinguishable boundaries of multiple tissues. And the enhanced representation ability of GA-Net also allows GA-Net to obtain well generalization performance on the limited dataset.

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Citation Download Citation

Jian Dai, Jayaram K. Udupa, Drew A. Torigian, Yubing Tong, Pengju Nie, Jing Zhang, Ran Li, Shiwei Han, and Tiange Liu "A deep-learning-based geographic attention model for body composition tissue segmentation", Proc. SPIE 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1246804 (10 April 2023); https://doi.org/10.1117/12.2653371

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