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Prostate segmentation in HIFU therapy.

Carole Garnier1, Jean-Jacques Bellanger, Ke Wu

  • 1Laboratoire Traitement du Signal et de l'Image, Inserm U642, Université de Rennes 1, F-35000 Rennes, France.

IEEE Transactions on Medical Imaging
|December 2, 2010
PubMed
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Accurate prostate segmentation in 3D transrectal ultrasound images aids high intensity focused ultrasound (HIFU) therapy planning. Novel semi-automatic methods achieved a mean surface distance of 0.77 mm for precise prostate delineation.

Area of Science:

  • Medical Imaging
  • Ultrasound Technology
  • Surgical Planning

Background:

  • Prostate segmentation is crucial for high intensity focused ultrasound (HIFU) therapy planning.
  • Accurate segmentation ensures effective and safe treatment delivery.
  • Current methods may require significant user interaction or lack precision.

Purpose of the Study:

  • To develop and evaluate semi-automatic methods for prostate segmentation in 3D transrectal ultrasound (TRUS) images.
  • To minimize user interaction while maximizing segmentation accuracy.
  • To assess the performance of different algorithmic approaches and configurations.

Main Methods:

  • Two semi-automatic segmentation approaches were developed: discrete dynamic contour and optimal surface detection.

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  • Methods were applied in 3D, with and without post-regularization, and on anisotropic/isotropic volumes.
  • Performance was evaluated on 28 3D TRUS images against expert delineations using various metrics.

    • Main Results:

    • The most efficient algorithm achieved a symmetric average surface distance of 0.77 mm.
    • Sequential combination of methods and post-regularization demonstrated improved performance.
    • The algorithms required minimal user interaction for effective prostate segmentation.

    Conclusions:

    • The presented semi-automatic methods offer a precise and efficient solution for prostate segmentation in 3D TRUS.
    • These techniques can significantly enhance the intra-operative planning of HIFU therapy.
    • The achieved accuracy supports the clinical applicability of these advanced segmentation tools.