Automated segmentation of polyps by 3D deep learning in photon-counting CT colonography

Abstract

Colorectal cancer (CRC) is the third most common cancer and second most common cause of cancer deaths. Most CRCs develop from large colorectal polyps, but most polyps remain smaller than 6 mm and will never develop into cancer. Therefore, conservative selective polypectomy based on polyp size would be a much more effective colorectal screening strategy than the current practice of removing all polyps. For this purpose, automated polyp measurement would be more reproducible and perhaps more precise than manual polyp measurement in CT colonography. However, for an accurate and explainable image-based measurement, it is first necessary to determine the 3D region of the polyp. We investigated the polyp segmentation performance of a traditional 3D U-Net, transformer-based U-Net, and denoising diffusion-based U-Net on a photon-counting CT (PCCT) colonography dataset. The networks were trained on 946 polyp volumes of interest (VOIs) collected from conventional clinical CT colonography datasets, and they were tested on 17 polyp VOIs extracted from a PCCT colonography dataset of an anthropomorphic colon phantom. All three segmentation networks yielded satisfactory segmentation accuracies with average Dice scores ranging between 0.73-0.75. These preliminary results and experiences are expected to be useful in guiding the development of a deep-learning tool for reliable estimation of the polyp size for the diagnosis and management of patients in CRC screening.