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Proceedings Paper

Automated detection of ureteral wall thickening on multi-detector row CT urography
Author(s): Lubomir Hadjiiski; Berkman Sahiner; Elaine M. Caoili; Richard H. Cohan; Chuan Zhou; Heang-Ping Chan
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Paper Abstract

We are developing a computer-aided detection (CAD) system for automated detection of ureteral wall thickening on multi-detector row CT urography, which potentially can assist radiologists in detecting ureter cancer. In the first stage of our CAD system, given a starting point, the ureter is tracked based on the CT values of the contrast-filled lumen. In the second stage, the ureter wall is segmented and the ureter wall thickness is estimated based on polar transformation, separation of the ureter wall from the background and measuring the wall thickness. In this pilot study, a limited data set of 20 patients with 22 abnormal ureters was used. Fourteen patients had a total of 16 ureters with malignant ureteral wall thickening. Two of the patients had malignant wall thickening in both the left and right ureters. The other six patients had 6 ureters with benign ureteral wall thickening. All malignant wall thickenings were biopsy-proven. The benign thickenings were determined by biopsy or by 2-year follow-up. In addition 3 normal ureters were used to determine the false positive (FP) detection rate of the CAD system. The tracking program successfully tracked the 25 ureters (22 abnormal and 3 normal) and detected 90% (20/22) of the ureters having wall thickening with 2.3 (7/3) FPs per ureter. 93% (15/16) of the ureters with malignant wall thickening and 83% (5/6) of the ureters with benign wall thickening were detected. The missed ureteral wall thickenings were developed asymmetrically around the part of the ureter filled with contrast and the detection criteria in our current CAD system was not able to identify them reliably. The preliminary results show that our detection system can track the ureter and can detect ureteral wall thickening.

Paper Details

Date Published: 17 March 2008
PDF: 7 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152U (17 March 2008); doi: 10.1117/12.771325
Show Author Affiliations
Lubomir Hadjiiski, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Elaine M. Caoili, Univ. of Michigan (United States)
Richard H. Cohan, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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