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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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An open source auto-segmentation algorithm for delineating heart and substructures - Development and validation

Agon Olloni1, Ebbe Laugaard Lorenzen2, Stefan Starup Jeppesen1

  • 1Department of Oncology, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Denmark; Academy of Geriatric Cancer Research (AgeCare), Odense University Hospital, Denmark.

Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology
|December 20, 2023
PubMed
Summary

A new hybrid auto-segmentation method accurately delineates the heart and its substructures for improved radiation dose estimation in thoracic cancer patients, aiding heart toxicity studies.

Keywords:
Automatic segmenationBreast CancerChambersCoronary arteriesHeartHybrid segmentationLung CancerMulti-atlasnnU-net

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Area of Science:

  • Medical imaging and radiation oncology.
  • Computational anatomy and image segmentation.

Background:

  • Thoracic cancer irradiation poses risks of heart toxicity.
  • Accurate heart dose estimation is crucial for mitigating these risks.
  • Automated segmentation of the heart and its substructures can aid dose assessment.

Purpose of the Study:

  • To develop and evaluate a hybrid automatic segmentation method for the heart and its substructures.
  • To assess the accuracy of this method in dose prediction for heart toxicity studies.

Main Methods:

  • A hybrid segmentation approach combining nnU-net with atlas- and model-based techniques.
  • Segmentation included the heart, four chambers, three large vessels, and coronary arteries.
  • Validation performed on a breast cancer dataset, with dose prediction accuracy evaluated against manual delineations.

Main Results:

  • The hybrid method achieved a high Dice Similarity Coefficient (DSC) of 0.95 for the heart.
  • Segmentation accuracy for heart chambers was comparable to inter-observer variability (DSC 0.78-0.86).
  • Automatic segmentation precisely predicted dose distribution, matching clinical experts' accuracy.

Conclusions:

  • The hybrid segmentation method accurately delineates cardiac structures for radiation therapy.
  • The method enables precise dose prediction, facilitating large-scale heart toxicity research.
  • The developed delineation algorithm will be publicly released.