Home > Published Issues > 2021 > Volume 9, No. 1, March 2021 >

Lesion Image Generation Using Conditional GAN for Metastatic Liver Cancer Detection

Yusuke Ikeda 1, Keisuke Doman 1, Yoshito Mekada 1, and Shigeru Nawano 2
1. Graduate School of Engineering, Chukyo University, Japan
2. International University of Health and Welfare Mita Hospital, Japan

Abstract—In the diagnosis of the abdomen, CT images taken under various conditions are visually checked by multiple doctors. Since diagnosing CT images requires doctors to take time and effort, a Computer-Aided Diagnosis system (CAD) based on a machine learning technique is expected. It is, however, difficult to collect a large number of case images for machine learning. In this paper, we propose a method to generate lesion images by a Conditional Generative Adversarial Networks (CGAN) and show the effectiveness of the proposed method by the accuracy of liver cancer detection from CT images. A CGAN which generates pseudo lesion images is trained with real lesion images labeled with “edge” and “non-edge” of the liver. We confirmed that the proposed method achieved the detection rate of 0.85 and the false positives per case of 0.20. The detection accuracy was higher than that of a conventional method.

Index Terms—CT image, deep learning, computer aided diagnosis, image synthesis

Cite: Yusuke Ikeda, Keisuke Doman, Yoshito Mekada, and Shigeru Nawano, "Lesion Image Generation Using Conditional GAN for Metastatic Liver Cancer Detection," Journal of Image and Graphics, Vol. 9, No. 1, pp. 27-30, March 2021. doi: 10.18178/joig.9.1.27-30

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.