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 2 hours ago
JSON representation

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 (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-05-21T17:43:12.000Z (6 months ago)
• Last Synced: 2024-05-22T18:08:19.938Z (6 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

README

        
[![Build Status](https://github.com/imagingdatacommons/highdicom/actions/workflows/run_unit_tests.yml/badge.svg)](https://github.com/imagingdatacommons/highdicom/actions)

[![Documentation Status](https://readthedocs.org/projects/highdicom/badge/?version=latest)](https://highdicom.readthedocs.io/en/latest/?badge=latest)

[![PyPi Distribution](https://img.shields.io/pypi/v/highdicom.svg)](https://pypi.python.org/pypi/highdicom/)

[![Python Versions](https://img.shields.io/pypi/pyversions/highdicom.svg)](https://pypi.org/project/highdicom/)

[![Downloads](https://pepy.tech/badge/highdicom)](https://pepy.tech/project/highdicom)

[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](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/)