Automated segmentation of urinary bladder and detection of bladder lesions in multi-detector row CT urography

Lubomir Hadjiiski; Berkman Sahiner; Heang-Ping Chan; Elaine M. Caoili; Richard H. Cohan; Chuan Zhou

doi:10.1117/12.813864

27 February 2009 Automated segmentation of urinary bladder and detection of bladder lesions in multi-detector row CT urography

Lubomir Hadjiiski, Berkman Sahiner, Heang-Ping Chan, Elaine M. Caoili, Richard H. Cohan, Chuan Zhou

Author Affiliations +

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72603R (2009) https://doi.org/10.1117/12.813864
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

We are developing a CAD system for automated bladder segmentation and detection of bladder lesions on MDCT urography, which potentially can assist radiologists in detecting bladder cancer. In the first stage of our CAD system, given a starting point, the bladder is segmented based on 3D region growing and active contours. In the second stage, lesion candidates are detected using histogram and shape analysis to separate the abnormality from the background, which is the bladder partially filled with contrast material. In this pilot study, a limited data set of 15 patients with 29 biopsy-proven lesions (26 malignant, 3 benign) was used. The average size for the 26 malignant lesions was 10 mm (range: 4.2 mm - 30.5mm) with conspicuity in the range of 2 to 5 on a 5-point scale (5=very subtle). The average size for the 3 benign lesions was 14 mm (range: 3.5 mm - 25mm) with conspicuity in the range of 2 to 3. Our segmentation program successfully segmented both the contrast and non-contrast part of the bladder in 87% (13/15) of the patients. The contrast-filled bladder region was successfully segmented for all 15 patients. Our system detected 83% (24/29) of the lesions with 1.4 (21/15) false positives per patient. 85% (22/26) of the bladder cancers were detected. The main cause for missed lesions was that they were in the non-contrast bladder region, which was not included in the detection stage in this pilot study. The results demonstrate the feasibility of developing a CAD system for automated segmentation of the bladder and detection of bladder malignancies.

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Lubomir Hadjiiski, Berkman Sahiner, Heang-Ping Chan, Elaine M. Caoili, Richard H. Cohan, and Chuan Zhou "Automated segmentation of urinary bladder and detection of bladder lesions in multi-detector row CT urography", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603R (27 February 2009); https://doi.org/10.1117/12.813864

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