{"id":13545792,"url":"https://github.com/ImagingDataCommons/highdicom","last_synced_at":"2025-04-02T17:31:47.467Z","repository":{"id":38237475,"uuid":"200103563","full_name":"ImagingDataCommons/highdicom","owner":"ImagingDataCommons","description":"High-level DICOM abstractions for the Python programming language ","archived":false,"fork":false,"pushed_at":"2024-05-21T17:43:12.000Z","size":3943,"stargazers_count":160,"open_issues_count":31,"forks_count":31,"subscribers_count":9,"default_branch":"master","last_synced_at":"2024-05-22T18:08:19.938Z","etag":null,"topics":["dicom","numpy","pydicom","python3","typehints"],"latest_commit_sha":null,"homepage":"https://highdicom.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ImagingDataCommons.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"code_of_conduct.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-08-01T18:50:40.000Z","updated_at":"2024-05-27T17:25:34.894Z","dependencies_parsed_at":"2023-10-14T14:05:59.204Z","dependency_job_id":"689b81a8-bb2b-4636-a8db-a17fffb355fb","html_url":"https://github.com/ImagingDataCommons/highdicom","commit_stats":null,"previous_names":[],"tags_count":42,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ImagingDataCommons%2Fhighdicom","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ImagingDataCommons%2Fhighdicom/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ImagingDataCommons%2Fhighdicom/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ImagingDataCommons%2Fhighdicom/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ImagingDataCommons","download_url":"https://codeload.github.com/ImagingDataCommons/highdicom/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246860093,"owners_count":20845598,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dicom","numpy","pydicom","python3","typehints"],"created_at":"2024-08-01T12:00:17.915Z","updated_at":"2025-04-02T17:31:47.458Z","avatar_url":"https://github.com/ImagingDataCommons.png","language":"Python","readme":"[![Build Status](https://github.com/imagingdatacommons/highdicom/actions/workflows/run_unit_tests.yml/badge.svg)](https://github.com/imagingdatacommons/highdicom/actions)\n[![Documentation Status](https://readthedocs.org/projects/highdicom/badge/?version=latest)](https://highdicom.readthedocs.io/en/latest/?badge=latest)\n[![PyPi Distribution](https://img.shields.io/pypi/v/highdicom.svg)](https://pypi.python.org/pypi/highdicom/)\n[![Python Versions](https://img.shields.io/pypi/pyversions/highdicom.svg)](https://pypi.org/project/highdicom/)\n[![Downloads](https://pepy.tech/badge/highdicom)](https://pepy.tech/project/highdicom)\n[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](code_of_conduct.md)\n\n# Highdicom\n\n`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:\n\n1. 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.\n2. Storing image-derived information, for example from computational analyses or human annotation, in derived DICOM objects for communication and storage. This includes:\n    * Annotations\n    * Parametric Map images\n    * Segmentation images\n    * Structured Report documents (containing numerical results, qualitative evaluations, and/or vector graphic annotations)\n    * Secondary Capture images\n    * Key Object Selection documents\n    * Legacy Converted Enhanced CT/PET/MR images (e.g., for single frame to multi-frame conversion)\n    * Softcopy Presentation State instances (including Grayscale, Color, and Pseudo-Color)\n3. Reading existing derived DICOM files and filtering and accessing the information contained within them.\n\n## Documentation\n\nPlease 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.\n\n## Citation\n\nFor more information about the motivation of the library and the design of highdicom's API, please see the following article:\n\n\u003e [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)\n\u003e 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.\n\u003e Journal of Digital Imaging, August 2022\n\nIf you use highdicom in your research, please cite the above article.\n\n## Support\n\nThe developers gratefully acknowledge their support:\n* The [Alliance for Digital Pathology](https://digitalpathologyalliance.org/)\n* The [MGH \u0026 BWH Center for Clinical Data Science](https://www.ccds.io/)\n* [Quantitative Image Informatics for Cancer Research (QIICR)](https://qiicr.org/)\n* [Radiomics](https://www.radiomics.io/)\n\nThis software is maintained in part by the [NCI Imaging Data Commons](https://imaging.datacommons.cancer.gov/) project,\nwhich has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071\nunder contract no. HHSN261201500003l.\n","funding_links":[],"categories":["Uncategorized","Libraries"],"sub_categories":["Uncategorized","Python"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FImagingDataCommons%2Fhighdicom","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FImagingDataCommons%2Fhighdicom","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FImagingDataCommons%2Fhighdicom/lists"}