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Updated: May 8, 2026

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Linking RayStation AI auto-contouring with Eclipse TPS: a scripted workflow for clinical integration.

Adam Ryczkowski1, Agata Jodda1, Tomasz Piotrowski1,2,3

  • 1Department of Medical Physics, Greater Poland Cancer Centre, Poznan, Poland.

Reports of Practical Oncology and Radiotherapy : Journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

This study automates AI-driven auto-contouring between RayStation and Eclipse treatment planning systems, significantly reducing contouring time and improving consistency in radiotherapy planning.

Keywords:
DICOM automationRayStation - Eclipse integrationartificial intelligence in radiation oncologydeep learning auto-contouringradiotherapy treatment planning

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Artificial Intelligence in Medicine

Background:

  • Accurate delineation of target volumes and organs-at-risk (OARs) is crucial for effective radiotherapy planning.
  • Deep learning-based auto-contouring offers improved consistency and reduced manual effort but faces integration challenges across different platforms.
  • This work addresses the need for seamless integration of AI auto-contouring tools into existing radiotherapy workflows.

Purpose of the Study:

  • To develop and validate an automated integration solution for AI-based auto-contouring from RayStation within the Eclipse treatment planning system (TPS).
  • To streamline the process of transferring AI-generated contours between different radiotherapy software platforms.
  • To enhance the efficiency and consistency of radiotherapy planning by leveraging cross-platform AI capabilities.

Main Methods:

  • Developed custom C# scripts for Eclipse and Python scripts for RayStation to facilitate automated data exchange via DICOM networking.
  • Implemented an automated workflow where Eclipse exports CT images and structure lists to RayStation.
  • Configured RayStation 2024B to automatically trigger Python scripts for deep learning segmentation and export contours back to Eclipse without user intervention.

Main Results:

  • Validated the automated integration on 35 clinical cases, demonstrating a complete process time averaging 1.1 minutes per case.
  • Achieved significant reductions in contouring time compared to manual methods.
  • Demonstrated improved consistency in contouring across multiple cases.

Conclusions:

  • The automated cross-platform integration enables direct access to RayStation's deep learning segmentation within Eclipse, providing a scalable contouring solution.
  • This integration enhances planning efficiency, reduces inter-observer variability, and allows clinicians to utilize advanced AI tools within their current TPS.
  • The modular framework promotes multi-vendor system interoperability in radiotherapy planning and is adaptable to evolving clinical protocols.