https://github.com/ImagingDataCommons/highdicom

High-level DICOM abstractions for the Python programming language
https://github.com/ImagingDataCommons/highdicom

dicom numpy pydicom python3 typehints

Last synced: 3 months ago
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High-level DICOM abstractions for the Python programming language

• Host: GitHub
• URL: https://github.com/ImagingDataCommons/highdicom
• Owner: ImagingDataCommons
• License: mit
• Created: 2019-08-01T18:50:40.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2024-03-27T16:09:01.000Z (3 months ago)
• Last Synced: 2024-03-27T16:53:23.700Z (3 months ago)
• Topics: dicom, numpy, pydicom, python3, typehints
• Language: Python
• Homepage: https://highdicom.readthedocs.io
• Size: 3.68 MB
• Stars: 154
• Watchers: 10
• Forks: 30
• Open Issues: 33
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: code_of_conduct.md
  • Citation: CITATION.cff

Lists

• awesome-HealthIT - HighDICOM - High-level DICOM abstractions for the Python programming language (Uncategorized / Uncategorized)
• awesome-dicom - highdicom - 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. (Libraries / Python)

README

        
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# 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/)