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Proceedings Paper

Ureter segmentation in CT urography (CTU) by COMPASS with multiscale Hessian enhancement
Author(s): Duncan Fairbanks; Lubomir Hadjiiski; Chuan Zhou; Heang-Ping Chan; Richard H. Cohan; Elaine M. Caoili; Kenny Cha
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Paper Abstract

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.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141S (20 March 2015); doi: 10.1117/12.2082639
Show Author Affiliations
Duncan Fairbanks, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Richard H. Cohan, Univ. of Michigan (United States)
Elaine M. Caoili, Univ. of Michigan (United States)
Kenny Cha, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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