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: about 1 month 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 6 years ago)
• Default Branch: master
• Last Pushed: 2024-05-21T17:43:12.000Z (12 months ago)
• Last Synced: 2024-05-22T18:08:19.938Z (12 months ago)
• Topics: dicom, numpy, pydicom, python3, typehints
• Language: Python
• Homepage: https://highdicom.readthedocs.io
• Size: 3.76 MB
• Stars: 160
• Watchers: 9
• Forks: 31
• Open Issues: 31
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: code_of_conduct.md
  • Citation: CITATION.cff

Awesome Lists containing this project

• 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)

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# Highdicom

`highdicom` is a pure Python package providing a high-level application programming interface (API) for working with DICOM files, with a focus on common operations required for machine learning, computer vision, and other similar computational analyses. Broadly speaking the package helps with three types of task:

1. Reading existing DICOM image files of a wide variety of modalities (covering radiology, pathology, and more) and formatting the frames to prepare them for computational analysis.

2. Storing image-derived information, for example from computational analyses or human annotation, in derived DICOM objects for communication and storage. This includes:

    * Annotations

    * Parametric Map images

    * Segmentation images

    * Structured Report documents (containing numerical results, qualitative evaluations, and/or vector graphic annotations)

    * 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)

3. Reading existing derived DICOM files and filtering and accessing the information contained within them.

## 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://link.springer.com/article/10.1007/s10278-022-00683-y)

> 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.

> Journal of Digital Imaging, August 2022

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/)

This software is maintained in part by the [NCI Imaging Data Commons](https://imaging.datacommons.cancer.gov/) project,

which has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071

under contract no. HHSN261201500003l.