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Proceedings Paper

Automatic lung nodule graph cuts segmentation with deep learning false positive reduction
Author(s): Wenqing Sun; Xia Huang; Tzu-Liang Bill Tseng; Wei Qian
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Paper Abstract

To automatic detect lung nodules from CT images, we designed a two stage computer aided detection (CAD) system. The first stage is graph cuts segmentation to identify and segment the nodule candidates, and the second stage is convolutional neural network for false positive reduction. The dataset contains 595 CT cases randomly selected from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) and the 305 pulmonary nodules achieved diagnosis consensus by all four experienced radiologists were our detection targets. Consider each slice as an individual sample, 2844 nodules were included in our database. The graph cuts segmentation was conducted in a two-dimension manner, 2733 lung nodule ROIs are successfully identified and segmented. With a false positive reduction by a seven-layer convolutional neural network, 2535 nodules remain detected while the false positive dropped to 31.6%. The average F-measure of segmented lung nodule tissue is 0.8501.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343M (3 March 2017); doi: 10.1117/12.2251302
Show Author Affiliations
Wenqing Sun, Univ. of Texas at El Paso (United States)
Xia Huang, Univ. of Texas at El Paso (United States)
Tzu-Liang Bill Tseng, Univ. of Texas at El Paso (United States)
Wei Qian, Univ. of Texas at El Paso (United States)
Northeastern Univ. (China)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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