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CT urography: segmentation of urinary bladder using CLASS with local contour refinement.

Kenny Cha1, Lubomir Hadjiiski, Heang-Ping Chan

  • 1Department of Radiology, The University of Michigan, Ann Arbor, MI 48109-0904, USA.

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Summary

A new computerized system improves bladder segmentation on CT urography (CTU) for bladder cancer detection. The enhanced method, CLASS with local contour refinement (LCR), significantly improves accuracy over previous systems.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Urology

Background:

  • Accurate bladder segmentation in CT urography (CTU) is crucial for computer-aided detection of bladder cancer.
  • Partial filling of the bladder with and without contrast agents presents a significant segmentation challenge.
  • Previous conjoint level set analysis and segmentation (CLASS) system faced inaccuracies in contour merging.

Purpose of the Study:

  • To develop and evaluate an improved computerized system for bladder segmentation on CTU.
  • To address limitations of previous methods in handling contrast variations within the bladder.
  • To enhance the accuracy of bladder segmentation for improved computer-aided bladder cancer detection.

Main Methods:

  • Implementation of a local contour refinement (LCR) method, incorporating model-guided refinement (MGR) and energy-driven wavefront propagation (EDWP).
  • MGR addresses premature level set propagation in contrast-filled regions.
  • EDWP refines conjoint contours using regularized energies and decision rules derived from training data.
  • Evaluation on a dataset of 173 CTU cases (81 training, 92 testing) with 3D hand-segmented contours as the reference standard.

Main Results:

  • CLASS with LCR achieved significantly higher accuracy across all evaluated metrics compared to CLASS alone.
  • For the test set, CLASS with LCR yielded an average volume intersection ratio of 78.0 ± 14.7% and a Jaccard index of 73.8 ± 13.4%.
  • CLASS alone resulted in a lower average volume intersection ratio of 67.3 ± 14.3% and Jaccard index of 65.0 ± 13.3% on the test set (p < 0.001).

Conclusions:

  • The developed CLASS with LCR system demonstrates significant potential for accurate bladder segmentation in CT urography.
  • The LCR method effectively overcomes challenges posed by non-uniform contrast filling.
  • This advancement contributes to more reliable computer-aided detection of bladder cancer.