Computer-aided detection of bladder wall thickening in CT urography (CTU)

Kenny H. Cha; Lubomir M. Hadjiiski; Heang-Ping Chan; Elaine M. Caoili; Richard H. Cohan; Alon Z. Weizer; Marshall N. Gordon; Ravi K. Samala

doi:10.1117/12.2293844

27 February 2018 Computer-aided detection of bladder wall thickening in CT urography (CTU)

Kenny H. Cha, Lubomir M. Hadjiiski, Heang-Ping Chan, Elaine M. Caoili, Richard H. Cohan, Alon Z. Weizer, Marshall N. Gordon, Ravi K. Samala

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 1057529 (2018) https://doi.org/10.1117/12.2293844
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

We are developing a computer-aided detection system for bladder cancer in CT urography (CTU). Bladder wall thickening is a manifestation of bladder cancer and its detection is more challenging than the detection of bladder masses. We first segmented the inner and outer bladder walls using our method that combined deep-learning convolutional neural network with level sets. The non-contrast-enhanced region was separated from the contrast-enhanced region with a maximum-intensity-projection-based method. The non-contrast region was smoothed and gray level threshold was applied to the contrast and non-contrast regions separately to extract the bladder wall and potential lesions. The bladder wall was transformed into a straightened thickness profile, which was analyzed to identify regions of wall thickening candidates. Volume-based features of the wall thickening candidates were analyzed with linear discriminant analysis (LDA) to differentiate bladder wall thickenings from false positives. A data set of 112 patients, 87 with wall thickening and 25 with normal bladders, was collected retrospectively with IRB approval, and split into independent training and test sets. Of the 57 training cases, 44 had bladder wall thickening and 13 were normal. Of the 55 test cases, 43 had wall thickening and 12 were normal. The LDA classifier was trained with the training set and evaluated with the test set. FROC analysis showed that the system achieved sensitivities of 93.2% and 88.4% for the training and test sets, respectively, at 0.5 FPs/case.

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Kenny H. Cha, Lubomir M. Hadjiiski, Heang-Ping Chan, Elaine M. Caoili, Richard H. Cohan, Alon Z. Weizer, Marshall N. Gordon, and Ravi K. Samala "Computer-aided detection of bladder wall thickening in CT urography (CTU)", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057529 (27 February 2018); https://doi.org/10.1117/12.2293844

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KEYWORDS

Bladder

Image segmentation

Bladder cancer

Convolutional neural networks

Computed tomography

Computer aided diagnosis and therapy

Cancer

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