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In Journal of biomedical optics

SIGNIFICANCE : Colorectal cancer incidence has decreased largely due to detection and removal of polyps. Computer-aided diagnosis development may improve on polyp detection and discrimination.

AIM : To advance detection and discrimination using currently available commercial colonoscopy systems, we developed a deep neural network (DNN) separating the color channels from images acquired under narrow-band imaging (NBI) and white-light endoscopy (WLE).

APPROACH : Images of normal colon mucosa and polyps from colonoscopies were studied. Each color image was extracted based on the color channel: red/green/blue. A multilayer DNN was trained using one-channel, two-channel, and full-color images. The trained DNN was then tested for performance in detection of polyps.

RESULTS : The DNN performed better using full-colored NBI over WLE images in the detection of polyps. Furthermore, the DNN performed better using the two-channel red + green images when compared to full-color WLE images.

CONCLUSIONS : The separation of color channels from full-color NBI and WLE images taken from commercially available colonoscopes may improve the ability of the DNN to detect and discriminate polyps. Further studies are needed to better determine the color channels and combination of channels to include and exclude in DNN development for clinical use.

Lai Lily L, Blakely Andrew, Invernizzi Marta, Lin James, Kidambi Trilokesh, Melstrom Kurt A, Yu Kevin, Lu Thomas


artificial intelligence algorithms, color channel separation, colorectal cancer, deep learning, narrow-band imaging, polyp discrimination