Deep learning for improved polyp detection from synthetic narrow-band imaging

Mathias Ramm Haugland; Hemin Ali Qadir; Ilangko Balasingham

doi:10.1117/12.2653048

7 April 2023 Deep learning for improved polyp detection from synthetic narrow-band imaging

Mathias Ramm Haugland, Hemin Ali Qadir, Ilangko Balasingham

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124651N (2023) https://doi.org/10.1117/12.2653048
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

To cope with the growing prevalence of colorectal cancer (CRC), screening programs for polyp detection and removal have proven their usefulness. Colonoscopy is considered the best-performing procedure for CRC screening. To ease the examination, deep learning based methods for automatic polyp detection have been developed for conventional white-light imaging (WLI). Compared with WLI, narrow-band imaging (NBI) can improve polyp classification during colonoscopy but requires special equipment. We propose a CycleGAN-based framework to convert images captured with regular WLI to synthetic NBI (SNBI) as a pre-processing method for improving object detection on WLI when NBI is unavailable. This paper first shows that better results for polyp detection can be achieved on NBI compared to a relatively similar dataset of WLI. Secondly, experimental results demonstrate that our proposed modality translation can achieve improved polyp detection on SNBI images generated from WLI compared to the original WLI. This is because our WLI-to-SNBI translation model can enhance the observation of polyp surface patterns in the generated SNBI images.

Conference Presentation

Citation Download Citation

Mathias Ramm Haugland, Hemin Ali Qadir, and Ilangko Balasingham "Deep learning for improved polyp detection from synthetic narrow-band imaging", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124651N (7 April 2023); https://doi.org/10.1117/12.2653048

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available