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Related Experiment Videos

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations.

Nuwan D Nanayakkara1, Jagath Samarabandu, Aaron Fenster

  • 1Department of Electrical and Computer Engineering, University of Western Ontario, London, Ontario N6A5B9, Canada. nnanayak@uwo.ca

Physics in Medicine and Biology
|March 23, 2006
PubMed
Summary

A new semi-automatic algorithm accurately segments prostate boundaries in ultrasound images for brachytherapy. This discrete dynamic contour model reduces manual effort and improves reproducibility in prostate cancer treatment planning.

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Radiotherapy Physics

Background:

  • Accurate prostate segmentation is crucial for effective ultrasound-guided brachytherapy dose planning.
  • Manual segmentation is time-consuming, subjective, and prone to inter-observer variability.
  • Existing automated methods may struggle with image noise and artifacts common in transrectal ultrasound (TRUS).

Purpose of the Study:

  • To develop and evaluate a semi-automatic discrete dynamic contour (DDC) model-based algorithm for prostate segmentation in 2D TRUS images.
  • To improve the accuracy, efficiency, and reproducibility of prostate boundary delineation for brachytherapy.
  • To reduce user interaction and reliance on manual outlining.

Main Methods:

  • A multi-resolution DDC model refinement procedure is employed, starting at low resolution and progressing to higher resolutions.

Related Experiment Videos

  • A fuzzy inference system (FIS) incorporating domain knowledge and adaptive region-based operators guides contour deformation and edge enhancement.
  • Automatic vertex relocation ensures contour points adhere to the prostate boundary, minimizing post-initialization user input.

    • Main Results:

    • The algorithm achieved a mean contour distance of 2.70 +/- 0.51 pixels (0.54 +/- 0.10 mm) compared to expert manual outlines on 114 TRUS images from six patients.
    • The DDC model segmentation demonstrated robustness against variations in initial contour placement and parameter settings.
    • The method proved insensitive to image noise and artifacts, enhancing boundary delineation reliability.

    Conclusions:

    • The proposed semi-automatic DDC algorithm offers an accurate and reproducible method for prostate segmentation in TRUS images.
    • This approach significantly reduces the time and variability associated with manual segmentation for brachytherapy planning.
    • The algorithm's robustness and reduced user dependency make it a promising tool for improving prostate cancer treatment.