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[Automatic image segmentation for treatment planning in radiotherapy].

D Pasquier1, L Peyrodie, F Denis

  • 1Centre Galilée, polyclinique de la Louvière, 69 rue de la Louvière, 59000 Lille, France. davidpasquier@free.fr

Cancer Radiotherapie : Journal De La Societe Francaise De Radiotherapie Oncologique
|December 7, 2010
PubMed
Summary

Image segmentation automates radiotherapy volume definition, reducing time and variability. This review details segmentation principles, applications, and clinical results for improved radiation therapy.

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Image Processing

Context:

  • Conformal radiotherapy and image-guided radiotherapy (IGRT) are advancing cancer treatment.
  • Increased treatment complexity necessitates precise delineation of target and organ-at-risk volumes.
  • Manual contouring is time-consuming and prone to inter- and intra-observer variability.

Purpose:

  • To describe the fundamental principles of image segmentation in radiotherapy.
  • To explore the diverse applications of image segmentation within radiation oncology.
  • To present recent clinical outcomes and advancements in automated segmentation.

Summary:

  • Image segmentation techniques leverage computational power and advanced image processing to automate or semi-automate the definition of volumes of interest (VOIs) in radiotherapy.
  • This automation addresses the significant time burden and observer variability associated with manual contouring.
  • The article covers core segmentation methodologies, their practical uses, and emerging clinical evidence.

Impact:

  • Automated image segmentation promises to enhance the efficiency and consistency of radiotherapy planning.
  • Improved delineation accuracy can lead to more effective dose delivery and potentially better patient outcomes.
  • This review provides a comprehensive overview for clinicians and researchers navigating the evolving landscape of radiotherapy image analysis.