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U-Net based deep learning bladder segmentation in CT urography.

Xiangyuan Ma1,2,3, Lubomir M Hadjiiski1, Jun Wei1

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

Medical Physics
|February 9, 2019
PubMed
Summary
This summary is machine-generated.

A novel U-Net-based deep learning approach (U-DL) significantly improves bladder segmentation in computed tomography urography (CTU). This automated method offers greater accuracy and efficiency compared to previous techniques for bladder cancer assessment.

Keywords:
CT urographybladdercomputer-aided detectiondeep learningsegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate bladder segmentation in CT urography (CTU) is crucial for computer-assisted bladder cancer detection and treatment assessment.
  • Previous deep learning convolution neural network and level sets (DCNN-LS) methods required user input and struggled with complex cases, leading to inaccurate segmentation.

Purpose of the Study:

  • To develop and evaluate an automated U-Net-based deep learning (U-DL) approach for precise bladder segmentation in CTU.
  • To compare the performance of 2D U-DL and 3D U-DL models with varying resolutions and preprocessing steps against a baseline DCNN-LS method.

Main Methods:

  • A dataset of 173 CTU cases was used, with expert radiologist 3D hand outlines serving as the reference standard.
  • Developed an automated U-DL method for bladder segmentation, eliminating the need for user-input bounding boxes and postprocessing level sets.
  • Compared 2D and 3D U-DL models, different image resolutions, and automated cropping preprocessing steps.

Main Results:

  • The best U-DL models demonstrated statistically significant improvements across all segmentation accuracy measures (AVI, AVE, AAVE, AMD, AHD, AJI) compared to the baseline DCNN-LS method (P < 0.001).
  • The best 2D U-DL model achieved an average volume intersection ratio (AVI) of 93.4 ± 9.5% and an average Jaccard index (AJI) of 85.0 ± 11.3%.
  • The U-DL approach showed superior performance, particularly in cases with poor image quality or advanced bladder cancer infiltration.

Conclusions:

  • The automated U-Net-based deep learning (U-DL) approach provides more accurate bladder segmentation in CTU than the previous DCNN-LS method.
  • U-DL offers a more automated and robust solution for bladder segmentation, enhancing its utility in computer-assisted bladder cancer detection and treatment response assessment pipelines.