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

Polyps are critical abnormalities that may indicate the initial phases of colon cancer, making accurate detection and analysis essential in medical diagnostics. Traditional 2D medical imaging techniques provide limited insight into the shape and structure of polyps, which is where 3D reconstruction can play a transformative role. By converting 2D polyp images into 3D models, healthcare professionals can gain enhanced visualization, enabling better diagnosis, treatment planning, and surgical precision. This study demonstrates a robust process for reconstructing polyp images in 3D using the Kvasir-Seg dataset, which includes 1,000 polyp images with expertly annotated ground truth masks. Our methodology includes the generation of depth maps using the Intel DPT-Large model, the extraction of point clouds with a customized approach, and the construction of precise mesh objects with Open3D. We extract geometric properties, including linearity, planarity, and curvature change, from the point clouds and meshes for a more in-depth analysis of the 3D objects. The unsupervised analysis of the resulting density plots indicates the presence of two distinct clusters within the feature distributions, highlighting the significance of 3D reconstruction for improved polyp characterization. Furthermore, the evaluation included 2D features such as edge density, contrast, dissimilarity, homogeneity, energy, correlation, entropy, number of lines, circularity, and Gabor energy. The analysis of p-values reveals that the 3D features related to these two clusters show notably lower p-values, thereby demonstrating their significance in the comprehensive analysis.