3D Reconstruction of Colonic Polyps: A Methodology for Improved Visualization and Analysis

Abstract

Colorectal cancer is one of the most common and life-threatening diseases worldwide, and colonoscopy remains the most effective method for early detection and removal of precancerous polyps from the human colon. However, a major limitation of conventional colonoscopy is the restricted field of view of the endoscope, which often prevents the complete visualization of a polyp’s surface. Due to complex polyp shapes or difficult camera angles, certain regions of the polyp may remain unobserved during the procedure, increasing the risk of misdiagnosis or incomplete removal. In this research, a 3D reconstruction-based approach is proposed to improve the visualization and analysis of colonic polyps using 2D endoscopic images. The overall framework begins with organizing the dataset based on lesion and video annotations, followed by automatic frame selection using mask-based conditions, image contrast, feature points, and depth quality scores to select the most informative frames for reconstruction. To improve image quality, reflection removal is applied using the EndoSRR framework. Depth maps are then estimated using ZoeDepth, a state-of-the-art monocular depth estimation model. Using these depth maps, dense point clouds are generated, and Region of Interest (ROI) extraction is performed for accurate reconstruction of the polyp surface. Clean 3D meshes are constructed using the Ball Pivoting Algorithm (BPA), along with refinement and hole-filling techniques. Additionally, the reconstructed 3D models are analyzed through various geometric and structural features, including shape descriptors and curvature. Finally, silhouette projection and alignment validation are performed to compare the 3D reconstructed model with the original 2D mask, ensuring accuracy and reliability of the reconstruction. This 3D reconstruction pipeline provides enhanced visualization of colonic polyps, enabling better inspection of previously unobserved regions and supporting further quantitative analysis for future computer-aided diagnosis systems.