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Proceedings Paper

3D thyroid segmentation in CT using self-attention convolutional neural network
Author(s): Xiuxiu He; Bang Jun Guo; Yang Lei; Yingzi Liu; Tonghe Wang; Walter J. Curran; Long Jiang Zhang; Tian Liu; Xiaofeng Yang
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Paper Abstract

The thyroid gland is a butterfly-shaped organ and belongs to the endocrine system. The abnormality in shape and volume of thyroid can reveal the occurrence of various diseases. Ultrasound (US) imaging is currently the most popular diagnostic tool for diagnosing thyroid diseases. However, most physicians would still make decisions depending on computed tomography (CT) because of its excellent resolution to show more details of the thyroid and its surroundings. The thyroid CT imaging before surgery is important because it can assist in determining the anatomical distribution of a lesion and its involvement in adjacent organs or tissues. However, precise segmentation of the thyroid relies heavily on the experience of the physician and is very time-consuming. In this work, we propose to use a 3D deep attention U-Net method to segment the thyroid from CT image automatically. The quantitative evaluation of the segmentation performance of the proposed method, we calculated the Dice similarity coefficient (DSC), sensitivity, specificity, and mean surface distance (MSD) indices between the ground truth and automatic segmentation We demonstrated high accuracy and robustness of the proposed deep-learning-based segmentation method visually and quantitatively. The resultant DSC, precision, and recall were 85% ± 6%, 86% ± 5% and 90% ± 5%, respectively.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131445 (16 March 2020); doi: 10.1117/12.2549786
Show Author Affiliations
Xiuxiu He, Emory Univ. (United States)
Bang Jun Guo, Emory Univ. (United States)
Southern Medical Univ. (China)
Nanjing Univ. (China)
Yang Lei, Emory Univ. (United States)
Yingzi Liu, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Long Jiang Zhang, Southern Medical Univ. (China)
Nanjing Univ. (China)
Tian Liu, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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