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Machine Learning for Auto-Segmentation in Radiotherapy Planning.

K Harrison1, H Pullen1, C Welsh2

  • 1Cavendish Laboratory, University of Cambridge, Cambridge, UK.

Clinical Oncology (Royal College of Radiologists (Great Britain))
|January 8, 2022
PubMed
Summary
This summary is machine-generated.

Automatic segmentation (auto-segmentation) in radiotherapy planning reduces clinician time and variability. Machine learning and deep learning methods show promise for accurate and efficient auto-segmentation in clinical practice.

Keywords:
Auto-segmentationDeep learningMachine learningRadiotherapy planning

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Area of Science:

  • Radiotherapy
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Manual segmentation of target structures and organs at risk is essential but time-consuming and variable.
  • Automatic segmentation (auto-segmentation) aims to improve efficiency and consistency in radiotherapy planning.

Purpose of the Study:

  • To review auto-segmentation techniques and applications in radiotherapy planning.
  • To highlight the role of machine learning and deep learning in auto-segmentation.

Main Methods:

  • Overview of traditional auto-segmentation methods (intensity analysis, shape modeling, atlas-based).
  • Focus on machine learning and deep learning approaches, including convolutional neural networks.

Main Results:

  • Machine learning and computer vision have enabled accurate and efficient auto-segmentation.
  • The review surveys various techniques and their applications in radiotherapy.

Conclusions:

  • Machine learning-driven auto-segmentation holds significant potential for clinical settings.
  • Barriers to widespread adoption in routine practice need to be addressed.