Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study
This research is significant for improving the early detection and prevention of colorectal cancer by providing a more robust computer-aided polyp detection system for physicians during colonoscopy operations, addressing the limitations of current systems in real-world clinical settings.
This paper addresses the challenge of colorectal polyp detection in real-world colonoscopy scenarios, where existing CNN-based systems perform poorly due to image quality issues. The authors collected a private dataset of 7,313 images from 224 complete colonoscopy procedures and propose an integrated system architecture, demonstrating effective real-time polyp detection.
Colorectal polyps are abnormal tissues growing on the intima of the colon or rectum with a high risk of developing into colorectal cancer, the third leading cause of cancer death worldwide. Early detection and removal of colon polyps via colonoscopy have proved to be an effective approach to prevent colorectal cancer. Recently, various CNN-based computer-aided systems have been developed to help physicians detect polyps. However, these systems do not perform well in real-world colonoscopy operations due to the significant difference between images in a real colonoscopy and those in the public datasets. Unlike the well-chosen clear images with obvious polyps in the public datasets, images from a colonoscopy are often blurry and contain various artifacts such as fluid, debris, bubbles, reflection, specularity, contrast, saturation, and medical instruments, with a wide variety of polyps of different sizes, shapes, and textures. All these factors pose a significant challenge to effective polyp detection in a colonoscopy. To this end, we collect a private dataset that contains 7,313 images from 224 complete colonoscopy procedures. This dataset represents realistic operation scenarios and thus can be used to better train the models and evaluate a system's performance in practice. We propose an integrated system architecture to address the unique challenges for polyp detection. Extensive experiments results show that our system can effectively detect polyps in a colonoscopy with excellent performance in real time.