Development of screening system for laxative-free CT colonography using hybrid learning

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K07578
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 52040:Radiological sciences-related
• Research Institution: National Institute of Technology(KOSEN), Oshima College
• Principal Investigator: Tachibana Rie 大島商船高等専門学校, 情報工学科, 教授 (90435462)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: CTコロノグラフィ / 電子クレンジング / 大腸がん検診 / 深層学習

Outline of Final Research Achievements

In this study, we developed a self-learning GAN-based EC method using 3D images and a recycle-GAN-based EC method using virtual endoscopy images. The final goal was to implement the best EC method by combining the two methods, but the accuracy of the EC method using virtual endoscopy images has not been more effective. Therefore, the final goal could not have been achieved. However, the self-learning GAN-based EC method was able to perform EC with sub-voxel accuracy on a small dataset with no image annotations.

Free Research Field

医用画像処理

Academic Significance and Societal Importance of the Research Achievements

開発した自己学習型GANを用いた電子クレンジング手法は従来法に比べ,自然なクレンジング画像の生成を可能とした．臨床現場における使用が可能となれば，従来に比べCTコロノグラフィ検査による電子クレンジングの精度向上が期待でき，大腸がん検診における被験者の負担を減らす効果及び検診率向上が期待できる．