CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation

You-Bao Tang; Sooyoun Oh; Yu-Xing Tang; Jing Xiao; Ronald M. Summers

doi:10.1117/12.2512004

13 March 2019 CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation

You-Bao Tang, Sooyoun Oh, Yu-Xing Tang, Jing Xiao, Ronald M. Summers

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109503V (2019) https://doi.org/10.1117/12.2512004
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

As an important task in medical imaging analysis, automatic lymph node segmentation from computed tomography (CT) scans has been studied well in recent years, but it is still very challenging due to the lack of adequately-labeled training data. Manually annotating a large number of lymph node segmentations is expensive and time-consuming. For this reason, data augmentation can be considered as a surrogate of enriching the data. However, most of the traditional augmentation methods use a combination of affine transformations to manipulate the data, which cannot increase the diversity of the data’s contextual information. To mitigate this problem, this paper proposes a data augmentation approach based on generative adversarial network (GAN) to synthesize a large number of CT-realistic images from customized lymph node masks. In this work, the pix2pix GAN model is used due to its strength for image generation, which can learn the structural and contextual information of lymph nodes and their surrounding tissues from CT scans. With these additional augmented images, a robust U-Net model is learned for lymph node segmentation. Experimental results on NIH lymph node dataset demonstrate that the proposed data augmentation approach can produce realistic CT images and the lymph node segmentation performance is improved effectively using the additional augmented data, e.g. the Dice score increased about 2.2% (from 80.3% to 82.5%).

Citation Download Citation

You-Bao Tang, Sooyoun Oh, Yu-Xing Tang, Jing Xiao, and Ronald M. Summers "CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503V (13 March 2019); https://doi.org/10.1117/12.2512004

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