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Proceedings Paper

Automatic lung segmentation in low-dose CT image with contrastive attention module
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Paper Abstract

Automatic lung segmentation with severe pathology plays a significant role in the clinical application, which can save physicians’ efforts to annotate lung anatomy. Since the lung has fuzzy boundary in low-dose computed tomography (CT) images, and the tracheas and other tissues generally have the similar gray value as the lung, it is a challenging task to accurately segment lung. How to extract key features and remove background features is a core problem for lung segmentation. This paper introduces a novel approach for automatic segmentation of lungs in low-dose CT images. First, we propose a contrastive attention module, which generates a pair of foreground and background attention maps to guide feature learning of lung and background separately. Second, a triplet loss is used on three feature vectors from different regions to pull the features from the full image and the lung region close whereas pushing the features from background away. Our method was validated on a clinical data set of 78 CT scans using the four-fold cross validation strategy. Experimental results showed that our method achieved more accurate segmentation results than that of state-of-the-art approaches.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131333 (10 March 2020); doi: 10.1117/12.2548806
Show Author Affiliations
Changxing Yang, Soochow Univ. (China)
Haihong Tian, Soochow Univ. (China)
Dehui Xiang, Soochow Univ. (China)
Fei Shi, Soochow Univ. (China)
Weifang Zhu, Soochow Univ. (China)
Xinjian Chen, Soochow Univ. (China)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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