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, 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 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:

This software is maintained in part by the NCI Imaging Data Commons 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.