Comparative performance of self-supervised 3D-ResNet-GAN for electronic cleansing in single- and dual-energy CT colonography

Rie Tachibana; Janne J. Näppi; Toru Hironaka; Hiroyuki Yoshida

doi:10.1117/12.2582013

15 February 2021 Comparative performance of self-supervised 3D-ResNet-GAN for electronic cleansing in single- and dual-energy CT colonography

Rie Tachibana, Janne J. Näppi, Toru Hironaka, Hiroyuki Yoshida

Author Affiliations +

Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications; 1160116 (2021) https://doi.org/10.1117/12.2582013
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

CT colonography (CTC) uses abdominal CT scans to examine the colon for cancers and polyps. To visualize the complete region of the colon without the 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. We developed a self-supervised 3D generative adversarial network model based on residual blocks (ResBlocks), called 3D-ResNet-GAN, for performing electronic cleansing (EC) in CTC. In this model, the convolution layers of the generator network are implemented with ResBlocks for enhancing the EC performance of the generator network over that of our previously developed U-net-based 3D-GAN EC model. We compared the performance of the proposed selfsupervised 3D-ResNet-GAN EC scheme with that of the previous 3D-GAN EC scheme by the use of an anthropomorphic phantom and a clinical CTC case with submerged polyps in single-energy CTC and dual-energy CTC. Our preliminary quantitative evaluation based on the phantom indicated that the proposed 3D-ResNet-GAN EC scheme can yield a statistically significant improvement over the EC performance of our previous 3D-GAN EC scheme in both single-energy and dual-energy CTC. Our preliminary visual evaluation based on the clinical CTC case indicated that the use of ResBlocks and dual-energy CTC yields the best EC result.

Conference Presentation

Citation Download Citation

Rie Tachibana, Janne J. Näppi, Toru Hironaka, and Hiroyuki Yoshida "Comparative performance of self-supervised 3D-ResNet-GAN for electronic cleansing in single- and dual-energy CT colonography", Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 1160116 (15 February 2021); https://doi.org/10.1117/12.2582013

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