Unpaired medical image translation between portal-venous phase and non-contrast CT volumes for multi-organ segmentation

Chen Shen; Yuichiro Hayashi; Masahiro Oda; Kazunari Misawa; Kensaku Mori

doi:10.1117/12.2590639

20 April 2021 Unpaired medical image translation between portal-venous phase and non-contrast CT volumes for multi-organ segmentation

Chen Shen, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

Author Affiliations +

Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 117920Q (2021) https://doi.org/10.1117/12.2590639
Event: International Forum on Medical Imaging in Asia 2021, 2021, Taipei, Taiwan

Abstract

This paper proposes an unpaired medical image translation framework between portal-venous phase and non-contrast CT volumes. Image-to-image translation has immense potential application values in medical image analysis fields, such as segmentation. Currently, many deep learning-based segmentation methods have been proposed on contrast-enhanced CT volumes. However, for the patients who have contrast medium allergy, only non-contrast CT is available. Thus, segmentation using non-contrast CT volumes is also an important task. Image translation from non-contrast CT to contrast-enhanced CT is an alternative to solve this problem. In this work, we employed the cycle-consistent adversarial network (CycleGAN) and unpaired image-to-image network (UNIT) for image translation. To evaluate the translation performance for multi-organ segmentation, we trained a segmentation model using contrast-enhanced CT images with U-Net. Our experimental results show that image translation has a positive influence on multi-organ segmentation. The segmentation actuaries greatly improved by applying the image translation.

Citation Download Citation

Chen Shen, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, and Kensaku Mori "Unpaired medical image translation between portal-venous phase and non-contrast CT volumes for multi-organ segmentation", Proc. SPIE 11792, International Forum on Medical Imaging in Asia 2021, 117920Q (20 April 2021); https://doi.org/10.1117/12.2590639

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