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Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
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Semiautomatic 3-D prostate segmentation from TRUS images using spherical harmonics.

Ismail B Tutar1, Sayan D Pathak, Lixin Gong

  • 1Image Computing Systems Laboratory, Departments of Electrical Engineering and Bioengineering, University of Washington, Seattle, WA 98195, USA.

IEEE Transactions on Medical Imaging
|December 16, 2006
PubMed
Summary

A new method automates prostate boundary identification in ultrasound images for brachytherapy quality assessment. This fast and robust technique improves dosimetry accuracy, potentially enhancing patient outcomes and reducing costs.

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Area of Science:

  • Medical Physics
  • Radiology
  • Image Analysis

Background:

  • Prostate brachytherapy quality assessment requires precise dosimetry, ideally performed intraoperatively.
  • Current methods for identifying prostate boundaries in ultrasound images are time-consuming and lack robustness.
  • Accurate 3-D prostate boundary identification is crucial for real-time dosimetry during brachytherapy.

Purpose of the Study:

  • To develop and validate a novel, automated method for segmenting 3-D prostate boundaries from postimplant ultrasound images.
  • To significantly reduce the time required for prostate boundary delineation in the operating room.
  • To improve the accuracy and consistency of dosimetry during prostate brachytherapy.

Main Methods:

  • A novel segmentation method using an optimization framework with shape constraints derived from statistical analysis of prostate shape models (spherical harmonics).
  • User initialization followed by automated boundary identification.
  • Validation using 30 postimplant ultrasound datasets with manual outlines from three experts serving as ground truth.

Main Results:

  • The automated algorithm achieved an average segmentation time of 2 minutes after user initialization.
  • The mean absolute distance error was 1.26 +/- 0.41 mm, with a percent volume overlap of 83.5 +/- 4.2%.
  • Segmentation error was found to be slightly lower than the interobserver variability observed in manual outlining.

Conclusions:

  • The proposed method offers a fast and robust solution for 3-D prostate boundary segmentation in ultrasound images.
  • This automated approach can facilitate intraoperative dosimetry, potentially improving prostate brachytherapy outcomes.
  • The accuracy of the segmentation is clinically relevant and comparable to expert manual delineation.