Title :
Predicting Polyp Location on Optical Colonoscopy From CT Colonography by Minimal-Energy Curve Modeling of the Colonoscope Path
Author :
Jiamin Liu ; Chang, K.W. ; Jianhua Yao ; Summers, R.M.
Author_Institution :
Dept. of Radiol. & Imaging Sci., Nat. Inst. of Health, Bethesda, MD, USA
Abstract :
The ability to accurately locate a polyp found on computed tomographic colonography (CTC) at subsequent optical colonoscopy (OC) is an important task in colorectal cancer screening. We present a method to more accurately match polyp locations at CTC and OC. A colonoscope was modeled as a flexible tube with negligible stretch and minimal strain. The path of the colonoscope was estimated using a minimal-energy curve method. The energy function was defined and optimized by a subdivision scheme. The prediction of polyp locations at OC from CTC was converted to an optimization problem. The prediction performance was evaluated on 134 polyps by comparing the predicted with the true polyp locations at OC. The method can accurately predict polyp locations at OC to within ±0.5 colonoscope mark (5 cm) for more than 58% of polyps and to within ±1 colonoscope mark (10 cm) for more than 96% of polyps, significantly improving upon previously published methods. This method can be easily incorporated into routine OC practice and allow the colonoscopist to begin the examination by targeting locations of potential polyps found at CTC.
Keywords :
biomedical optical imaging; cancer; computerised tomography; endoscopes; optimisation; colonoscope path minimal-energy curve modeling; colonoscopist; colorectal cancer screening; energy function; minimal-energy curve method; on computed tomographic colonography; optical colonoscopy; optimization problem; polyp location; size 5 cm to 10 cm; subdivision scheme; Cancer; Colonoscopy; Computed tomography; Curve fitting; Splines (mathematics); Tumors; CT colonography; Colonic polyps; minimal-energy curve; optical colonoscopy; Algorithms; Colonic Polyps; Colonography, Computed Tomographic; Humans; Image Processing, Computer-Assisted; Linear Models; Models, Biological; Optical Imaging;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2012.2217960