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Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing
05:25

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Published on: August 14, 2019

A coupled level set framework for bladder wall segmentation with application to MR cystography.

Chaijie Duan1, Zhengrong Liang, Shangliang Bao

  • 1Beijing Key Lab of Medical Physics and Engineering, Peking University, Beijing 100871, China.

IEEE Transactions on Medical Imaging
|March 5, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel coupled level set framework for automatic bladder wall segmentation using T(1)-weighted MRI. The method accurately measures bladder wall thickness, outperforming existing models.

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Area of Science:

  • Medical Imaging
  • Image Segmentation
  • Computational Anatomy

Background:

  • Accurate bladder wall segmentation is crucial for clinical applications like virtual cystoscopy.
  • Existing methods often struggle with automatic delineation of both inner and outer bladder wall borders.
  • T(1)-weighted MRI presents unique challenges due to decreased urine intensity.

Purpose of the Study:

  • To develop a coupled level set (LS) framework for automatic bladder wall segmentation using T(1)-weighted MR images.
  • To enable accurate voxel-by-voxel bladder wall thickness measurement.
  • To improve upon existing bladder segmentation techniques for clinical applications such as MR cystography.

Main Methods:

  • A novel coupled level set framework utilizing two collaborative LS functions and a regional adaptive clustering algorithm.
  • Automatic delineation of both inner and outer bladder wall borders, requiring only manual seed point selection.
  • An image energy function robust to inhomogeneity, motion artifacts, and noise by considering global and local image intensity distributions.
  • Bladder wall thickness measurement based on the integral path length between the segmented borders.

Main Results:

  • The proposed framework successfully segmented both inner and outer bladder wall borders automatically.
  • The method demonstrated adaptability to T(1)-weighted MR images with decreased urine intensity.
  • Expert evaluation showed statistically significant improvement in detecting inner and outer borders compared to the Chan-Vese (C-V) model.
  • The framework proved immune to common image artifacts like inhomogeneity and noise.

Conclusions:

  • The coupled level set framework offers a robust and accurate solution for bladder wall segmentation in T(1)-weighted MRI.
  • This advancement facilitates precise bladder wall thickness measurement for applications like MR cystography.
  • The developed method represents a significant improvement over previous bladder segmentation techniques.