Automatic detection of ureter lesions in CT urography

Trevor Exell; Lubomir Hadjiiski; Heang-Ping Chan; Kenny H. Cha; Elaine M. Caoili; Richard H. Cohan M.D.; Jun Wei; Chuan Zhou

doi:10.1117/12.2216866

24 March 2016 Automatic detection of ureter lesions in CT urography

Trevor Exell, Lubomir Hadjiiski, Heang-Ping Chan, Kenny H. Cha, Elaine M. Caoili, Richard H. Cohan M.D., Jun Wei, Chuan Zhou

Author Affiliations +

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97851O (2016) https://doi.org/10.1117/12.2216866
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

We are developing a CAD system for automated detection of ureter abnormalities in multi-detector row CT urography (CTU). Our CAD system consists of two stages. The first stage automatically tracks the ureter via the previously proposed COmbined Model-guided Path-finding Analysis and Segmentation System (COMPASS). The second stage consists of lesion enhancement filtering, adaptive thresholding, edge extraction, and noise removal. With IRB approval, 36 cases were collected from patient files, including 15 cases (17 ureters with 32 lesions) for training, and 10 abnormal cases (11 ureters with 17 lesions) and 11 normal cases (22 ureters) for testing. All lesions were identified by experienced radiologists on the CTU images and COMPASS was able to track the ureters in 100% of the cases. The average lesion size was 5.1 mm (range: 2.1 mm – 21.9 mm) for the training set and 6.1 mm (range: 2.0 mm – 18.9 mm) for the test set. The average conspicuity was 4.1 (range: 2 to 5) and 3.9 (range: 1 to 5) on a scale of 1 to 5 (5 very subtle), for the training and test sets, respectively. The system achieved 90.6% sensitivity at 2.41 (41/17) FPs/ureter for the training set and 70.6% sensitivity at 2 (44/22) FPs/normal ureter for the test set. These initial results demonstrate the feasibility of the CAD system to track the ureter and detect ureter cancer of medium conspicuity and relatively small sizes.

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Trevor Exell, Lubomir Hadjiiski, Heang-Ping Chan, Kenny H. Cha, Elaine M. Caoili, Richard H. Cohan M.D., Jun Wei, and Chuan Zhou "Automatic detection of ureter lesions in CT urography", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851O (24 March 2016); https://doi.org/10.1117/12.2216866

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