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Electronic cleansing in CT colonography using a generative adversarial network
Author(s): Rie Tachibana; Janne J. Näppi; Toru Hironaka; Hiroyuki Yoshida
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Paper Abstract

We developed a novel 3D electronic cleansing (EC) method for CT colonography (CTC) based on a generative adversarial network (GAN). GANs are machine-learning algorithms that can be trained to translate an input image directly into a desired output image without using explicit manual annotations. A 3D-GAN EC scheme was developed by extending a 2D-pix2pix GAN model to volumetric CTC datasets based on 3D-convolutional kernels. To overcome the usual need for paired input-output training data, the 3D-GAN model was trained by use of a self-supervised learning scheme where the training data were constructed iteratively as a combination of volumes of interest (VOIs) from paired anthropomorphic colon phantom CTC datasets and input VOIs from the unseen clinical input CTC dataset where the virtually cleansed output sample pairs were self-generated by use of a progressive cleansing method. Our preliminary evaluation with a clinical fecal-tagging CTC case showed that the 3D-GAN EC scheme can substantially reduce the processing time and EC image artifacts in comparison to our previous deep-learning EC scheme.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 1095419 (15 March 2019); doi: 10.1117/12.2512466
Show Author Affiliations
Rie Tachibana, Massachusetts General Hospital (United States)
National Institute of Technology, Oshima College (Japan)
Janne J. Näppi, Massachusetts General Hospital (United States)
Toru Hironaka, Massachusetts General Hospital (United States)
Hiroyuki Yoshida, Massachusetts General Hospital (United States)

Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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