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Proceedings Paper

Pulmonary nodules segmentation method based on auto-encoder
Author(s): Guodong Zhang; Mao Guo; Zhaoxuan Gong; Jing Bi; Yoohwan Kim; Wei Guo
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Paper Abstract

In this paper, we proposed a semi-automatic pulmonary nodule segmentation algorithm, which is operated within a region of interest for each nodule. It mainly includes two parts: the unsupervised training of auto-encoder and the supervised training of segmentation network. Applying an auto-encoder's unsupervised learning, we obtain a feature extractor that consists of its encoded part. Through adding some new neural network layers behind the feature extractor and do supervised learning on it, we get the final segmentation neural network. Compared with the traditional maximum two-dimensional entropy threshold segmentation algorithm, the dice correlation coefficient of this algorithm is 1% - 9% higher in 36 regions of interest segmentation experiments.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062P (9 August 2018); doi: 10.1117/12.2502835
Show Author Affiliations
Guodong Zhang, Shenyang Aerospace Univ. (China)
Univ. of Nevada, Las Vegas (United States)
Mao Guo, Shenyang Aerospace Univ. (China)
Zhaoxuan Gong, Shenyang Aerospace Univ. (China)
Jing Bi, Shenyang Aerospace Univ. (China)
Yoohwan Kim, Univ. of Nevada, Las Vegas (United States)
Wei Guo, Shenyang Aerospace Univ. (China)
Shenyang Institute of Computing Technology (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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