A deep learning-based image pre-processing pipeline for enhanced 3D colon surface reconstruction robust to endoscopic illumination artifacts

This contribution demonstrates the efficacy of targeted image pre-processing techniques in enhancing deep-learning-based 3D reconstruction of colon surfaces. It challenges the conventional approach of applying global image illumination corrections or only specular reflection removal in colonoscopy by advocating for the correction of local under-and over-exposures. Initially, an overview of the pipeline, encompassing image exposure correction coupled with a Recurrent Neural Network Simultaneous Localization and Mapping (RNN-SLAM) system is provided. Subsequently, this paper quantifies the reconstruction accuracy of endoscope trajectories within the colon, comparing results obtained with and without appropriate illumination correction. Notably, the results underscore the significant impact of the Endo-LMSPEC method on trajectory accuracy. Through targeted exposure correction, the average pose error (APE) is notably reduced, accompanied by a decrease in the root mean square error (RMSE). Moreover, the median pose error experiences a substantial improvement, highlighting the robustness of the Endo-LMSPEC method in mitigating local illumination artifacts. These findings underscore the critical role of tailored image pre-processing techniques in achieving more accurate and reliable 3D reconstructions of colon surfaces during endoscopic procedures.