{"id":18785272,"url":"https://github.com/dbouget/raidionics_rads_lib","last_synced_at":"2026-01-16T16:31:47.883Z","repository":{"id":109859272,"uuid":"488624409","full_name":"dbouget/raidionics_rads_lib","owner":"dbouget","description":"Processing backend for Raidionics to execute pipelines performing tumor segmentation and standardized reporting (RADS)","archived":false,"fork":false,"pushed_at":"2026-01-15T09:23:11.000Z","size":47405,"stargazers_count":3,"open_issues_count":1,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2026-01-15T15:30:50.750Z","etag":null,"topics":["ct","mri","raidionics","reporting-and-data-system"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dbouget.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2022-05-04T14:35:00.000Z","updated_at":"2026-01-15T09:17:37.000Z","dependencies_parsed_at":"2025-08-06T15:05:38.832Z","dependency_job_id":"a89c2be8-5fb7-4c69-94ad-5df082b40206","html_url":"https://github.com/dbouget/raidionics_rads_lib","commit_stats":null,"previous_names":[],"tags_count":9,"template":false,"template_full_name":null,"purl":"pkg:github/dbouget/raidionics_rads_lib","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbouget%2Fraidionics_rads_lib","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbouget%2Fraidionics_rads_lib/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbouget%2Fraidionics_rads_lib/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbouget%2Fraidionics_rads_lib/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dbouget","download_url":"https://codeload.github.com/dbouget/raidionics_rads_lib/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbouget%2Fraidionics_rads_lib/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28479903,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-16T11:59:17.896Z","status":"ssl_error","status_checked_at":"2026-01-16T11:55:55.838Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ct","mri","raidionics","reporting-and-data-system"],"created_at":"2024-11-07T20:46:07.091Z","updated_at":"2026-01-16T16:31:47.870Z","avatar_url":"https://github.com/dbouget.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Raidionics processing backend for performing segmentation and computation of standardized report (RADS)\n\n[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)\n[![](https://img.shields.io/badge/python-3.9|3.10|3.11|3.12|3.13-blue.svg)](https://www.python.org/downloads/)\n[![Paper](https://zenodo.org/badge/DOI/10.3389/fneur.2022.932219.svg)](https://www.frontiersin.org/articles/10.3389/fneur.2022.932219/full)\n[![PyPI version](https://img.shields.io/pypi/v/raidionicsrads.svg)](https://pypi.org/project/raidionicsrads/)\n[![codecov](https://codecov.io/gh/dbouget/raidionics_rads_lib/branch/master/graph/badge.svg?token=ZSPQVR7RKX)](https://codecov.io/gh/dbouget/raidionics_rads_lib)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/dbouget/ae5f318af4826ef8bf5ff0f27e7c0817/01_run_simple_segmentation.ipynb)\n\u003ca target=\"_blank\" href=\"https://huggingface.co/spaces/dbouget/raidionics\"\u003e\u003cimg src=\"https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-yellow.svg\"\u003e\u003c/a\u003e\n\nThe code corresponds to the Raidionics backend for running processing pipelines over MRI/CT scans. The segmentation of\na few organs or tumor types, as well as the generation of standardized reports are included.  \nThe module can either be used as a Python library, as CLI, or as Docker container.\n\n## [Installation](https://github.com/dbouget/raidionics_rads_lib#installation)\n\n```\npip install raidionicsrads\nor\npip install git+https://github.com/dbouget/raidionics_rads_lib.git\n```\n\n## [Getting started](https://github.com/dbouget/raidionics_rads_lib#getting-started)\n\n### [Notebooks](https://github.com/dbouget/raidionics_rads_lib#notebooks)\n\nBelow are Jupyter Notebooks including different examples on how to get started with the segmentation and reporting tasks,\neither over the brain or mediastinal area.\n\n\u003cdiv style=\"display: flex;\"\u003e\n  \u003cdiv style=\"flex: 1; margin-right: 20px;\"\u003e\n\n| Notebook                       | Clinical target     | Colab                                                                                                                                                                                                                                            | GitHub                                                                                                                                                                                              |\n|--------------------------------|---------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **Preoperative Segmentation**  | :brain: Neuro       | \u003ca href=\"https://colab.research.google.com/gist/dbouget/ae5f318af4826ef8bf5ff0f27e7c0817/01_run_simple_segmentation.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e        | [![View on GitHub](https://img.shields.io/badge/View%20on%20GitHub-blue?logo=github)](https://github.com/dbouget/raidionics_rads_lib/blob/master/notebooks/01_run_simple_segmentation.ipynb)        |\n| **Preoperative Segmentation**  | :lungs: Mediastinum | \u003ca href=\"https://colab.research.google.com/gist/dbouget/b88aa64b6a87f85a6b644ab4bcf65560/05_run_segmentation_mediastinum.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e   | [![View on GitHub](https://img.shields.io/badge/View%20on%20GitHub-blue?logo=github)](https://github.com/dbouget/raidionics_rads_lib/blob/master/notebooks/05_run_segmentation_mediastinum.ipynb)   |\n| **Postoperative Segmentation** | :brain: Neuro       | \u003ca href=\"https://colab.research.google.com/gist/dbouget/094c24f3e67deae6a1424a399cb7dcd7/03_run_postoperative_segmentation.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e | [![View on GitHub](https://img.shields.io/badge/View%20on%20GitHub-blue?logo=github)](https://github.com/dbouget/raidionics_rads_lib/blob/master/notebooks/03_run_postoperative_segmentation.ipynb) |\n| **Simple Reporting**           | :brain: Neuro       | \u003ca href=\"https://colab.research.google.com/gist/dbouget/68a50c14b1805254b4eff6fb2e3b8fe5/02_run_simple_reporting.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e           | [![View on GitHub](https://img.shields.io/badge/View%20on%20GitHub-blue?logo=github)](https://github.com/dbouget/raidionics_rads_lib/blob/master/notebooks/02_run_simple_reporting.ipynb)           |\n| **Surgical Reporting**         | :brain: Neuro       | \u003ca href=\"https://colab.research.google.com/gist/dbouget/19800ffd443ed0f27724f28b100533ce/04_run_surgical_reporting.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e         | [![View on GitHub](https://img.shields.io/badge/View%20on%20GitHub-blue?logo=github)](https://github.com/dbouget/raidionics_rads_lib/blob/master/notebooks/04_run_surgical_reporting.ipynb)         |\n| **Batch processing**           | :brain: Neuro       | -                                                                                                                                                                                                                                                | [![View on GitHub](https://img.shields.io/badge/View%20on%20GitHub-blue?logo=github)](https://github.com/dbouget/raidionics_rads_lib/blob/master/tests/batch_iterative_process_example.py)          |\n\n  \u003c/div\u003e\n\u003c/div\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\n### [CLI](https://github.com/dbouget/raidionics_rads_lib#cli)\n\u003c/summary\u003e\n\n```\nraidionicsrads -c CONFIG (-v debug)\n```\n\nCONFIG should point to a configuration file (*.ini), specifying all runtime parameters,\naccording to the pattern from [**blank_main_config.ini**](https://github.com/dbouget/raidionics-rads-lib/blob/master/blank_main_config.ini).\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\n### [Python module](https://github.com/dbouget/raidionics_rads_lib#python-module)\n\u003c/summary\u003e\n\n```\nfrom raidionicsrads.compute import run_rads\nrun_rads(config_filename=\"/path/to/main_config.ini\")\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\n### [Docker](https://github.com/dbouget/raidionics_rads_lib#docker)\n\u003c/summary\u003e\n\nWhen calling Docker images, the --user flag must be properly used in order for the folders and files created inside\nthe container to inherit the proper read/write permissions. The user ID is retrieved on-the-fly in the following\nexamples, but it can be given in a more hard-coded fashion if known by the user.\n\n:warning: The Docker image can only perform inference using the CPU, there is no GPU support at this stage.\n```\ndocker pull dbouget/raidionics-rads:v1.3-py39-cpu\n```\n\nFor opening the Docker image and interacting with it, run:  \n```\ndocker run --entrypoint /bin/bash -v /home/\u003cusername\u003e/\u003cresources_path\u003e:/workspace/resources -t -i --runtime=nvidia --network=host --ipc=host --user $(id -u) dbouget/raidionics-rads:v1.3-py39-cpu\n```\n\nThe `/home/\u003cusername\u003e/\u003cresources_path\u003e` before the column sign has to be changed to match a directory on your local \nmachine containing the data to expose to the docker image. Namely, it must contain folder(s) with images you want to \nrun inference on, as long as a folder with the trained models to use, and a destination folder where the results will \nbe placed.\n\nFor launching the Docker image as a CLI, run:  \n```\ndocker run -v /home/\u003cusername\u003e/\u003cresources_path\u003e:/workspace/resources -t -i --runtime=nvidia --network=host --ipc=host --user $(id -u) dbouget/raidionics-rads:v1.3-py39-cpu -c /workspace/resources/\u003cpath\u003e/\u003cto\u003e/main_config.ini -v \u003cverbose\u003e\n```\n\nThe `\u003cpath\u003e/\u003cto\u003e/main_config.ini` must point to a valid configuration file on your machine, as a relative path to the `/home/\u003cusername\u003e/\u003cresources_path\u003e` described above.\nFor example, if the file is located on my machine under `/home/myuser/Data/RADS/main_config.ini`, \nand that `/home/myuser/Data` is the mounted resources partition mounted on the Docker image, the new relative path will be `RADS/main_config.ini`.  \nThe `\u003cverbose\u003e` level can be selected from [debug, info, warning, error].\n\n\u003c/details\u003e\n\n## [How to cite](https://github.com/dbouget/raidionics_rads_lib#how-to-cite)\nIf you are using Raidionics in your research, please cite the following references.\n\nThe final software including updated performance metrics for preoperative tumors and introducing postoperative tumor segmentation:\n```\n@article{bouget2023raidionics,\n    author = {Bouget, David and Alsinan, Demah and Gaitan, Valeria and Holden Helland, Ragnhild and Pedersen, André and Solheim, Ole and Reinertsen, Ingerid},\n    year = {2023},\n    month = {09},\n    pages = {},\n    title = {Raidionics: an open software for pre-and postoperative central nervous system tumor segmentation and standardized reporting},\n    volume = {13},\n    journal = {Scientific Reports},\n    doi = {10.1038/s41598-023-42048-7},\n}\n```\n\nFor the preliminary preoperative tumor segmentation validation and software features:\n```\n@article{bouget2022preoptumorseg,\n    title={Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting},\n    author={Bouget, David and Pedersen, André and Jakola, Asgeir S. and Kavouridis, Vasileios and Emblem, Kyrre E. and Eijgelaar, Roelant S. and Kommers, Ivar and Ardon, Hilko and Barkhof, Frederik and Bello, Lorenzo and Berger, Mitchel S. and Conti Nibali, Marco and Furtner, Julia and Hervey-Jumper, Shawn and Idema, Albert J. S. and Kiesel, Barbara and Kloet, Alfred and Mandonnet, Emmanuel and Müller, Domenique M. J. and Robe, Pierre A. and Rossi, Marco and Sciortino, Tommaso and Van den Brink, Wimar A. and Wagemakers, Michiel and Widhalm, Georg and Witte, Marnix G. and Zwinderman, Aeilko H. and De Witt Hamer, Philip C. and Solheim, Ole and Reinertsen, Ingerid},\n    journal={Frontiers in Neurology},\n    volume={13},\n    year={2022},\n    url={https://www.frontiersin.org/articles/10.3389/fneur.2022.932219},\n    doi={10.3389/fneur.2022.932219},\n    issn={1664-2295}\n}\n```\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\n## [Models](https://github.com/dbouget/raidionics_rads_lib#models)\n\u003c/summary\u003e\n\nThe trained models are automatically downloaded when running Raidionics or Raidionics-Slicer.\nAlternatively, all existing Raidionics models can be browsed [here](https://github.com/raidionics/Raidionics-models/releases/tag/v1.3.0-rc) directly.\n\u003c/details\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbouget%2Fraidionics_rads_lib","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdbouget%2Fraidionics_rads_lib","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbouget%2Fraidionics_rads_lib/lists"}