[](https://github.com/imagingdatacommons/highdicom/actions)
[](https://highdicom.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.python.org/pypi/highdicom/)
[](https://pypi.org/project/highdicom/)
[](https://pepy.tech/project/highdicom)
[](code_of_conduct.md)
# High DICOM
A library that provides high-level DICOM abstractions for the Python programming language to facilitate the creation and handling of DICOM objects for image-derived information, including image annotations, and image analysis results.
It currently provides tools for creating and decoding the following DICOM information object definitions (IODs):
* Annotations
* Parametric Map images
* Segmentation images
* Structured Report documents
* Secondary Capture images
* Key Object Selection documents
* Legacy Converted Enhanced CT/PET/MR images (e.g., for single frame to multi-frame conversion)
* Softcopy Presentation State instances (including Grayscale, Color, and Pseudo-Color)
## Documentation
Please refer to the online documentation at [highdicom.readthedocs.io](https://highdicom.readthedocs.io), which includes installation instructions, a user guide with examples, a developer guide, and complete documentation of the application programming interface of the `highdicom` package.
## Citation
For more information about the motivation of the library and the design of highdicom's API, please see the following article:
> [Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology](https://arxiv.org/abs/2106.07806)
> C.P. Bridge, C. Gorman, S. Pieper, S.W. Doyle, J.K. Lennerz, J. Kalpathy-Cramer, D.A. Clunie, A.Y. Fedorov, and M.D. Herrmann
If you use highdicom in your research, please cite the above article.
## Support
The developers gratefully acknowledge their support:
* The [Alliance for Digital Pathology](https://digitalpathologyalliance.org/)
* The [MGH & BWH Center for Clinical Data Science](https://www.ccds.io/)
* [Quantitative Image Informatics for Cancer Research (QIICR)](https://qiicr.org/)
* [Radiomics](https://www.radiomics.io/)
* The [NCI Imaging Data Commons](https://imaging.datacommons.cancer.gov/)
Raw data
{
"_id": null,
"home_page": null,
"name": "highdicom",
"maintainer": "Markus D. Herrmann, Christopher P. Bridge",
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": null,
"author": "Markus D. Herrmann, Christopher P. Bridge",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/1f/26/f3d945aa5b243e1136fff677319e19e3d6ce5df29fdc07b18fb6edfaf21e/highdicom-0.25.0.tar.gz",
"platform": null,
"description": "[](https://github.com/imagingdatacommons/highdicom/actions)\n[](https://highdicom.readthedocs.io/en/latest/?badge=latest)\n[](https://pypi.python.org/pypi/highdicom/)\n[](https://pypi.org/project/highdicom/)\n[](https://pepy.tech/project/highdicom)\n[](code_of_conduct.md)\n\n# High DICOM\n\nA library that provides high-level DICOM abstractions for the Python programming language to facilitate the creation and handling of DICOM objects for image-derived information, including image annotations, and image analysis results.\nIt currently provides tools for creating and decoding the following DICOM information object definitions (IODs):\n* Annotations\n* Parametric Map images\n* Segmentation images\n* Structured Report documents\n* Secondary Capture images\n* Key Object Selection documents\n* Legacy Converted Enhanced CT/PET/MR images (e.g., for single frame to multi-frame conversion)\n* Softcopy Presentation State instances (including Grayscale, Color, and Pseudo-Color)\n\n## Documentation\n\nPlease refer to the online documentation at [highdicom.readthedocs.io](https://highdicom.readthedocs.io), which includes installation instructions, a user guide with examples, a developer guide, and complete documentation of the application programming interface of the `highdicom` package.\n\n## Citation\n\nFor more information about the motivation of the library and the design of highdicom's API, please see the following article:\n\n> [Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology](https://arxiv.org/abs/2106.07806)\n> C.P. Bridge, C. Gorman, S. Pieper, S.W. Doyle, J.K. Lennerz, J. Kalpathy-Cramer, D.A. Clunie, A.Y. Fedorov, and M.D. Herrmann\n\nIf you use highdicom in your research, please cite the above article.\n\n## Support\n\nThe developers gratefully acknowledge their support:\n* The [Alliance for Digital Pathology](https://digitalpathologyalliance.org/)\n* The [MGH & BWH Center for Clinical Data Science](https://www.ccds.io/)\n* [Quantitative Image Informatics for Cancer Research (QIICR)](https://qiicr.org/)\n* [Radiomics](https://www.radiomics.io/)\n* The [NCI Imaging Data Commons](https://imaging.datacommons.cancer.gov/)\n",
"bugtrack_url": null,
"license": "LICENSE",
"summary": "High-level DICOM abstractions.",
"version": "0.25.0",
"project_urls": {
"documentation": "https://highdicom.readthedocs.io/",
"homepage": "https://github.com/imagingdatacommons/highdicom",
"repository": "https://github.com/ImagingDataCommons/highdicom.git"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "796b608d442cc958c9c7fdf8f441189fd6c0061a20590a106f3d9a5d2c7b20fd",
"md5": "42865236e82ded07c8acc9030b8a44a3",
"sha256": "73e5b3985284c0800ae71568111316fbc0c0b931e378edb975115b3fc9876164"
},
"downloads": -1,
"filename": "highdicom-0.25.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "42865236e82ded07c8acc9030b8a44a3",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 1089129,
"upload_time": "2025-02-09T23:53:43",
"upload_time_iso_8601": "2025-02-09T23:53:43.304054Z",
"url": "https://files.pythonhosted.org/packages/79/6b/608d442cc958c9c7fdf8f441189fd6c0061a20590a106f3d9a5d2c7b20fd/highdicom-0.25.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "1f26f3d945aa5b243e1136fff677319e19e3d6ce5df29fdc07b18fb6edfaf21e",
"md5": "e038075df4708f8626542d05dc896e67",
"sha256": "1bd5592e216d4f76723e14978e7e6da253f753d917f12e7d78d19ad3063d9d78"
},
"downloads": -1,
"filename": "highdicom-0.25.0.tar.gz",
"has_sig": false,
"md5_digest": "e038075df4708f8626542d05dc896e67",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 1102504,
"upload_time": "2025-02-09T23:53:45",
"upload_time_iso_8601": "2025-02-09T23:53:45.461000Z",
"url": "https://files.pythonhosted.org/packages/1f/26/f3d945aa5b243e1136fff677319e19e3d6ce5df29fdc07b18fb6edfaf21e/highdicom-0.25.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-02-09 23:53:45",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "imagingdatacommons",
"github_project": "highdicom",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "highdicom"
}
JSON
