Customized CycleGAN with balanced skip-networked residual layer (CCGAN-BSNRL) to enhance colonoscopy image by narrow-band imaging translation

Abstract

Colonoscopy can achieve significantly enhanced polyp detection by employing narrowband imaging (NBI) along with conventional white light (WL) imaging. However, problems associated with switching between two modalities during colonoscopy hinder its widespread clinical application. This study proposes a novel skip-connected residual layer-based customized CycleGAN model, which we refer to as customized CycleGAN with balanced skip-networked residual layer (CCGAN-BSNRL), for achieving an effective translation of WL colonoscopy images into NBI-like images. We chose the residual layer for customization as it is crucial for capturing the core spatial features of the input and translating them into the target domain. To evaluate, we have chosen a set of existing metrics to compare images generated by our model with images generated by other related models. We additionally propose those metrics for the comprehensive evaluation of other generative models’ generated images. Finally, a simple segmentation model is used to inspect the improvement in polyp detection. Considering the overall results, with a significantly enhanced detection of polyps achieved through the translated NBI (Dice score greater than 6%, Jaccard score greater than 8%, on average) from the proposed model, it is expected that the CCGAN-BSNRL model should positively impact the diagnosis accuracy of colorectal cancer in clinical applications.