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Automatic Segmentation with Deep Learning in Radiotherapy.

Lars Johannes Isaksson1,2, Paul Summers3, Federico Mastroleo1,4

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This summary is machine-generated.

This review analyzes 807 deep learning studies for automatic segmentation in radiotherapy, offering practical guidelines for future research in medical image segmentation and radiation therapy.

Keywords:
artificial intelligenceartificial neural networksautomaticdeep learningradiotherapysegmentation

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Area of Science:

  • Radiotherapy
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Deep learning is increasingly used for automatic segmentation in radiotherapy.
  • Existing research spans various cancer sites, imaging modalities (CT, MRI, PET), and segmentation techniques.
  • A comprehensive overview of current studies is needed to identify trends and research gaps.

Purpose of the Study:

  • To formally review and analyze the landscape of deep learning-based automatic segmentation in radiotherapy.
  • To uncover commonalities, trends, and methods across 807 published papers.
  • To provide actionable insights and practical guidelines for researchers in the field.

Main Methods:

  • Systematic review of 807 published papers on deep learning for automatic segmentation in radiotherapy.
  • Collection and analysis of key statistics regarding cancer sites, image types, and segmentation methods.
  • Utilized ChatGPT for information condensation and analysis.

Main Results:

  • Identified commonalities and trends in deep learning segmentation studies within radiotherapy.
  • Highlighted areas requiring further research and investigation.
  • Provided practical guidelines for conducting effective segmentation studies.

Conclusions:

  • The review offers a structured overview of the current state of automatic segmentation in radiotherapy using deep learning.
  • Actionable insights and guidelines are provided to improve research practices and inform future studies.
  • This work serves as a valuable resource for researchers navigating the competitive field of medical image segmentation.