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Discrete deformable model guided by partial active shape model for TRUS image segmentation.

Pingkun Yan1, Sheng Xu, Baris Turkbey

  • 1Philips Research North America, Briarcliff Manor, NY 10510, USA. pingkun.yan@philips.com

IEEE Transactions on Bio-Medical Engineering
|February 10, 2010
PubMed
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This study introduces a new method for automatic prostate segmentation in transrectal ultrasound (TRUS) images. The approach effectively estimates missing prostate boundaries in shadow areas, improving segmentation accuracy.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computer-Aided Diagnosis

Background:

  • Accurate prostate segmentation in transrectal ultrasound (TRUS) is crucial for clinical applications.
  • Challenges include low signal-to-noise ratio (SNR) and obscured boundaries due to calcifications or dense tissues in TRUS images.
  • Existing methods often require manual correction, particularly in shadow regions.

Purpose of the Study:

  • To develop a novel, automated method for robust prostate segmentation in 2-D TRUS images.
  • To overcome limitations of current segmentation techniques, especially in handling shadow areas.
  • To eliminate the need for user interaction in shape correction within shadow regions.

Main Methods:

  • Utilizing a priori shapes estimated from partial contours for prostate segmentation.

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  • Employing a partial active shape model to estimate complete prostate shapes from incomplete boundary information.
  • Implementing a discrete deformable model with dynamic programming for efficient energy functional minimization.
  • Adopting a multiresolution approach for enhanced robustness and computational efficiency.

    • Main Results:

    • Successful automatic extraction of prostate boundaries from 2-D TRUS images.
    • Demonstrated effectiveness in segmenting prostates even with missing boundaries in shadow areas.
    • Achieved an average mean absolute distance error of 2.01 mm ± 1.02 mm on 301 images from 19 patients.

    Conclusions:

    • The proposed method offers a robust and automated solution for prostate segmentation in TRUS images.
    • The active shape model effectively addresses the challenge of missing boundaries in shadow regions.
    • This technique holds promise for improving clinical applications requiring accurate prostate delineation.