Pulmonary nodules segmentation method based on auto-encoder

Guodong Zhang; Mao Guo; Zhaoxuan Gong; Jing Bi; Yoohwan Kim; Wei Guo

doi:10.1117/12.2502835

9 August 2018 Pulmonary nodules segmentation method based on auto-encoder

Guodong Zhang, Mao Guo, Zhaoxuan Gong, Jing Bi, Yoohwan Kim, Wei Guo

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108062P (2018) https://doi.org/10.1117/12.2502835
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Guodong Zhang, Mao Guo, Zhaoxuan Gong, Jing Bi, Yoohwan Kim, and Wei Guo "Pulmonary nodules segmentation method based on auto-encoder", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062P (9 August 2018); https://doi.org/10.1117/12.2502835

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available