Motion-based Camera Localization System in Colonoscopy Videos
Heming Yao, Ryan W. Stidham, Zijun Gao, Jonathan Gryak, Kayvan, Najarian

TL;DR
This paper introduces a novel motion-based camera localization system for colonoscopy videos that estimates the camera's position and classifies colon segments, improving accuracy over existing methods.
Contribution
The study presents a self-training convolutional neural network for robust camera motion estimation and a colon template for accurate anatomical segmentation.
Findings
Superior performance of the proposed method over existing techniques
Average classification accuracy of 0.754 in clinical videos
Effective removal of non-informative frames enhances robustness
Abstract
Optical colonoscopy is an essential diagnostic and prognostic tool for many gastrointestinal diseases, including cancer screening and staging, intestinal bleeding, diarrhea, abdominal symptom evaluation, and inflammatory bowel disease assessment. Automated assessment of colonoscopy is of interest considering the subjectivity present in qualitative human interpretations of colonoscopy findings. Localization of the camera is essential to interpreting the meaning and context of findings for diseases evaluated by colonoscopy. In this study, we propose a camera localization system to estimate the relative location of the camera and classify the colon into anatomical segments. The camera localization system begins with non-informative frame detection and removal. Then a self-training end-to-end convolutional neural network is built to estimate the camera motion, where several strategies are…
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