Deep Learning-Based Semantic Segmentation for Objective Colonoscopy Quality Assessment.

ABSTRACT: Background: This study aims to objectively evaluate the overall quality of colonoscopies using a specially trained deep learning-based semantic segmentation neural network. This represents a modern and valuable approach for the analysis of colonoscopy frames. Methods: We collected thousands of colonoscopy frames extracted from a set of video colonoscopy files. A color-based image processing method was used to extract color features from specific regions of each colonoscopy frame, namely, the intestinal mucosa, residues, artifacts, and lumen. With these features, we automatically annotated all the colonoscopy frames and then selected the best of them to train a semantic segmentation network. This trained network was used to classify the four region types in a different set of

SUBMITTER: Vulpoi RA 

PROVIDER: S-EPMC11943454 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature