Exploratory Semantic Segmentation of Liver and Spleen Shadow Patterns for Colonoscopic Flexure Localisation

Hepatic and splenic flexures are important anatomical landmarks in colonoscopy, supporting segmental orientation, lesion localisation, and bowel preparation assessment. This exploratory study investigated whether liver- and spleen-related shadow patterns visible on the colonic wall could serve as indirect cues for flexure localisation. We developed a preliminary workflow combining colour-based annotation and deep learning semantic segmentation. Representative shadow and mucosal regions were used to extract colour characteristics, while lumen/borders and artefacts were identified using luminance-based criteria. Based on these rules, 500 colonoscopy frames with visible shadows were automatically annotated and used to train a segmentation network for four classes: shadows, intestinal mucosa, lumen/borders, and artefacts. The trained model was then applied frame by frame to colonoscopy videos to generate temporal profiles of the segmented regions. In a representative test video, shadow clusters appeared near expert-indicated flexure regions after empirical filtering of frames not matching several criteria. These preliminary findings suggest that liver- and spleen-related shadows may provide complementary cues for flexure localisation in selected cases. However, the method remains exploratory and requires validation on larger, independently annotated datasets.