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A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical

Maysam Shahedi1, Martin Halicek2, Rongrong Guo1

  • 1Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, 30322, USA.

Medical Physics
|April 4, 2018
PubMed
Summary

This study introduces a fast and accurate semiautomated method for segmenting the prostate in CT scans. The algorithm achieves results comparable to expert manual segmentation, improving treatment planning.

Keywords:
computer tomography (CT)prostatesegmentationtexture features

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Radiotherapy Planning

Background:

  • Prostate segmentation in CT images is crucial for radiotherapy and brachytherapy.
  • Manual segmentation is time-consuming and prone to interobserver variability due to low soft tissue contrast in CT.

Purpose of the Study:

  • To develop and evaluate a semiautomated, 3D segmentation method for prostate CT images.
  • The method utilizes shape and texture analysis to overcome limitations of manual segmentation.

Main Methods:

  • A point distribution model was generated using principal component analysis on surface points.
  • Local texture differences were analyzed across prostate surface subregions.
  • The algorithm combines learned shape and texture features with user input for segmentation.

Main Results:

  • The semiautomated method achieved an average Dice Similarity Coefficient (DSC) of 88% and Mean Absolute Distance (MAD) of 1.9 mm.
  • Results were comparable to interexpert variability (92% DSC, 1.1 mm MAD).

Conclusions:

  • The proposed algorithm offers fast, robust, and accurate 3D prostate segmentation in CT images.
  • It performs well without prior intrapatient segmentation data.
  • Accuracy approaches interexpert manual segmentation levels.