Share Email Print
cover

Proceedings Paper

Self-supervised generative adversarial network for electronic cleansing in dual-energy CT colonography
Author(s): Rie Tachibana; Janne J. Näppi; Toru Hironaka; Hiroyuki Yoshida
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

CT colonography (CTC) uses abdominal CT scans to examine the colon for cancers and polyps. To visualize the complete region of colon without possibly obstructing residual materials inside the colon, an orally administered contrast agent is used to opacify the residual fecal materials on CT images followed by virtual cleansing of the opacified materials from the images. However, current EC methods can introduce large numbers of residual image artifacts that complicate the interpretation of the virtually cleansed CTC images. Such artifacts can be resolved by use of dual-energy CTC (DE-CTC) that provides more information about the observed materials than does conventional single-energy CTC (SE-CTC). We generalized a 3D generative adversarial network (3D-GAN) model into a self-supervised electronic cleansing (EC) scheme for dual-energy CT colonography (DE-CTC). The 3D-GAN is used to transform the acquired DE-CTC volumes into a representative cleansed CTC volume by use of an iterative self-supervised method that adapts the scheme to the unique conditions of each case. Our preliminary evaluation with an anthropomorphic phantom indicated that the use of the 3DGAN EC scheme with DE-CTC features and the self-supervised scheme generates EC images of higher quality than those obtained by use of SE-CTC or conventional training samples only.

Paper Details

Date Published: 12 March 2020
PDF: 6 pages
Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 113181E (12 March 2020); doi: 10.1117/12.2549234
Show Author Affiliations
Rie Tachibana, Institute of National Colleges of Technology (Japan)
Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Janne J. Näppi, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Toru Hironaka, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 11318:
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Thomas M. Deserno, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray