Author:
Kim Byeong Soo,Cho Minwoo,Chung Goh Eun,Lee Jooyoung,Kang Hae Yeon,Yoon Dan,Cho Woo Sang,Lee Jung Chan,Bae Jung Ho,Kong Hyoun-Joong,Kim Sungwan
Abstract
AbstractRecognizing 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 posed by colonoscopy images through non-informative removal preprocessing. The image data is labeled by clinicians, and the system deduces section correspondence stochastically. The model categorizes the colon into three sections: right (cecum and ascending colon), middle (transverse colon), and left (descending colon, sigmoid colon, rectum). We estimated the appearance time of anatomical boundaries with an average error of 6.31 s for AO, 9.79 s for HF, 27.69 s for SF, and 3.26 s for outside of the body. The proposed method can facilitate future advancements towards AI-based automatic reporting, offering time-saving efficacy and standardization.
Funder
the Korea Medical Device Development Fund grant funded by the Korean government
the Ministry of Science and ICT, Korea under the Information Technology Research Center (ITRC) support program
Publisher
Springer Science and Business Media LLC
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