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Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing

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Segmentation of urinary bladder in CT urography.

Lubomir Hadjiiski¹, Heang-Ping Chan, Elaine M Caoili

¹University of Michigan, Department of Radiology, Ann Arbor, MI 48109, USA. lhadjisk@umich.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

We developed a Conjoint Level set Analysis and Segmentation System (CLASS) for bladder segmentation in CTU scans. CLASS effectively segments both contrast-filled and non-contrast regions, showing potential for computer-aided bladder cancer diagnosis.

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Urological Imaging

Background:

Accurate bladder segmentation in CTU is crucial for diagnosing bladder cancer.
Segmentation is challenging due to variable contrast enhancement (contrast-filled and no-contrast regions).

Purpose of the Study:

To develop and evaluate the Conjoint Level set Analysis and Segmentation System (CLASS) for automated bladder segmentation on CTU.
To assess CLASS's performance in segmenting bladders with varying degrees of contrast enhancement.

Main Methods:

CLASS employs a multi-stage approach including preprocessing, initial segmentation, 3D/2D level set segmentation, and post-processing.
The system separately segments no-contrast (NC) and contrast-filled (C) regions, combining them for the final contour.
Performance was evaluated on 70 bladders using a 5-point quality rating scale.

Main Results:

CLASS achieved high-quality segmentation, with average ratings of 4.0±0.7 for NC and 4.0±1.0 for C regions in partially filled bladders.
Overall, 66% of the 70 segmented bladders received quality ratings of 4 or above, with an average rating of 3.8±0.7.
High ratings (≥4) were achieved for 87% of NC and 77% of C regions in partially filled bladders.

Conclusions:

The Conjoint Level set Analysis and Segmentation System (CLASS) demonstrates significant potential for automated bladder segmentation in CTU.
The system's ability to handle varying contrast levels contributes to its effectiveness in computer-aided diagnosis of bladder conditions.