Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.

ABSTRACT: Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatomical characteristics of the colon pose challenges for establishing a reliable localization system. This study presents a system utilizing 100 colonoscopy videos, combining density clustering and deep learning. Cascaded CNN models are employed to estimate the appendix orifice (AO), flexures, and "outside of the body," sequentially. Subsequently, DBSCAN algorithm is applied to identify anatomical sections. Clustering-based analysis integrates clinical knowledge and context based on the anatomical section within the model. We address challenges po

SUBMITTER: Kim BS 

PROVIDER: S-EPMC10776865 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature