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Updated: Dec 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Library of deep-learning image segmentation and outcomes model-implementations.

Aditya P Apte1, Aditi Iyer1, Maria Thor1

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|May 7, 2020
PubMed
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This summary is machine-generated.

An open-source library integrates deep learning models for medical image segmentation and outcome prediction in radiotherapy and radiomics, enabling automated analysis and prognosis estimation.

Area of Science:

  • Radiotherapy and Medical Imaging
  • Computational Oncology
  • Artificial Intelligence in Healthcare

Background:

  • Automated treatment planning in oncology relies on advanced computational models.
  • Validation and integration of deep learning models in clinical workflows are essential for improving patient care.
  • Radiotherapy and radiomics data analysis requires robust and reproducible methodologies.

Purpose of the Study:

  • To present an open-source library of deep learning-based image segmentation and outcomes models for radiotherapy and radiomics.
  • To facilitate model validation, automated segmentation, ensemble creation, and clinical workflow integration.
  • To provide a fully automated and reproducible pipeline for prognosis estimation in oncology.

Main Methods:

  • Development of an integrated library within the Computational Environment for Radiological Research (CERR) software platform.
Keywords:
Deep-learningImage segmentationLibraryModel implementationsNormal tissue complicationRadiomicsRadiotherapy outcomesTumor control

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  • Implementation of deep learning models for image segmentation and outcomes prediction.
  • Distribution of segmentation models via containers for diverse computing architectures.
  • Inclusion of models based on Dose-Volume Histograms (DVH) and radiomics features from the Image Biomarker Standardization Initiative.

    • Main Results:

    • A centralized library of validated deep learning models for radiotherapy and radiomics is now available.
    • The library supports automated segmentation, model validation across institutions, and ensemble creation.
    • A reproducible pipeline for prognosis estimation is established by combining segmentation and outcomes models.
    • The library leverages CERR's feature extraction capabilities and supports various scientific computing environments.

    Conclusions:

    • The presented library enhances the automation and reproducibility of radiotherapy and radiomics analysis.
    • It enables seamless integration of validated deep learning models into clinical oncology workflows.
    • This resource is crucial for advancing automated treatment planning and improving prognostic accuracy in cancer care.