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):
- Segmentation images
- Structured Reports
- Secondary Capture images
- Legacy Converted Enhanced CT/PET/MR images (e.g. for single frame to multi-frame conversion)
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.
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
If you use highdicom in your research, please cite the above article.
The developers gratefully acknowledge their support: