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)

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:

• The Alliance for Digital Pathology
• The MGH & BWH Center for Clinical Data Science
• Quantitative Image Informatics for Cancer Research (QIICR)
• Radiomics
• The NCI Imaging Data Commons