Raidionics

Open-source software for automatic brain tumor segmentation and standardized clinical reporting from MRI.

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Raidionics segments brain tumors (pre- and postoperative) from MRI volumes, computes tumor characteristics, and generates a standardized clinical report — all through a single graphical interface. It's built by SINTEF Medical Image Analysis and is used both as a standalone desktop app and as a 3D Slicer plugin.

Under the hood, the app is a thin front end over a set of independent, reusable backend libraries — each usable on its own (Python package, CLI, or Docker), and each documented in its own repository.

flowchart TB
    subgraph FrontEnds["Front ends"]
        A["Raidionics<br/>(Desktop app)"]
        B["Raidionics-Slicer<br/>(3D Slicer plugin)"]
    end

    subgraph Backends["Backend libraries (pip / CLI / Docker)"]
        C["raidionics_rads_lib<br/>pipeline orchestration + reporting"]
        D["raidionics_seg_lib<br/>segmentation & classification"]
        E["validation_metrics_computation<br/>cross-validation & metrics"]
    end

    F["Raidionics-models<br/>trained model zoo and test resources"]

    A --> C
    B --> C
    C --> D
    C --> F
    D --> F
    E --> F

Note on repo ownership: the three backend libraries currently live under the maintainer's personal account (dbouget) rather than the raidionics org, while the front ends, models, and datasets are under raidionics. Both are part of the same project.

• Just want to use the software? → Download Raidionics for Windows, Ubuntu (≥18.04), macOS, or macOS ARM.
• Working in 3D Slicer? → Install the Raidionics-Slicer plugin.
• Building your own pipeline or research code? → Start with raidionics_seg_lib for segmentation alone, or raidionics_rads_lib for full pipelines including reporting.
• Validating a model? → validation_metrics_computation.

Each backend library can be installed via pip, run as a CLI, called as a Python module, or run in Docker — see the respective repo's README for details.

If you use any part of Raidionics in your research, please cite:

Main software release (pre- and postoperative segmentation, standardized reporting):

Bouget, D., Alsinan, D., Gaitan, V., Holden Helland, R., Pedersen, A., Solheim, O., & Reinertsen, I. (2023). Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting. Scientific Reports, 13. doi:10.1038/s41598-023-42048-7

Preliminary validation (preoperative segmentation methodology):

Bouget, D., Pedersen, A., Jakola, A.S., et al. (2022). Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting. Frontiers in Neurology, 13. doi:10.3389/fneur.2022.932219

Full BibTeX and CITATION.cff metadata are available in each individual repository.

Most repositories are distributed under the BSD-2-Clause license (datasets AeroPath/LyNoS use MIT) — see each repository for its specific license.