Mr. Duncan Fairbanks Profile

Duncan Fairbanks

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Proceedings Article | 20 March 2015 Paper

Ureter segmentation in CT urography (CTU) by COMPASS with multiscale Hessian enhancement

Duncan Fairbanks, Lubomir Hadjiiski, Chuan Zhou, Heang-Ping Chan, Richard Cohan, Elaine Caoili, Kenny Cha

Proceedings Volume 9414, 94141S (2015) https://doi.org/10.1117/12.2082639

KEYWORDS: Image segmentation, Error analysis, Bladder, Solid modeling, Cancer, Tissues, Feature extraction, 3D image processing, Image filtering

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We are developing an automated method for the segmentation of ureters in CTU, referred to as COmbined Modelguided Path-finding Analysis and Segmentation System (COMPASS). Ureter segmentation is a critical component for computer-aided detection of ureter cancer. A challenge for ureter segmentation is inconsistent opacification of the region of interest, which makes it difficult to be distinguished from other tissue in the surrounding area. COMPASS consists of four stages: (1) region finding and adaptive thresholding, (2) segmentation accuracy analysis, (3) potential backtracking and branching, and (4) edge profile extraction and feature analysis. In this study, we evaluated a new method in which CTU images were pre-processed with 3D multiscale Hessian filtering that enhances tubular structures. Our goal is to compare the performance of COMPASS with and without multiscale Hessian enhancement. With IRB approval, 79 cases with 124 ureters and 10 cases with 18 ureters were collected retrospectively from patient files as training and test sets, respectively. On average, the ureters spanned 289 CT slices (range: 115-405, median: 302). The segmentation performance was quantitatively assessed as the percentage of length of each ureter that was successfully tracked relative to manually tracking. COMPASS alone segmented, on average, 99.16% and 98.74% of each ureter in the training and test sets, respectively. COMPASS with Hessian enhancement segmented, on average, 97.89% and 99.63% of each ureter in the training and test sets, respectively. Although the difference did not reach statistical significance in this small test set, Hessian-enhanced tracking shows promise for overcoming certain types of difficult cases.

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