| Name | highdicom JSON |
| Version |
0.23.1
JSON |
| download |
| home_page | None |
| Summary | High-level DICOM abstractions. |
| upload_time | 2024-10-25 03:45:16 |
| maintainer | Markus D. Herrmann, Christopher P. Bridge |
| docs_url | None |
| author | Markus D. Herrmann |
| requires_python | >=3.10 |
| license | LICENSE |
| keywords |
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| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
[](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/)
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