highdicom
High-level DICOM abstractions.
Decision gist · record as of 2026-08-14
Yes. Highdicom is actively maintained, permissively licensed (MIT), has low install friction, and fills a genuine gap in DICOM tooling for computational workflows. It is well-supported by the imaging research community (NCI Imaging Data Commons, QIICR, Radiomics) and has no known security vulnerabilities. Install it if you work with DICOM files in machine learning or computational analysis contexts.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- JPEG-LS compressed images require pyjpegls, which may need system libraries depending on your platform.
- Low friction installation with pure Python distribution.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for research and production deployments.
last release 2026-07-28 (17 days) · last repo commit 2026-08-07 · 238 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 83,636 downloads/mo, #14,060 on PyPI
Alternatives
Verify before relying
pip install highdicom
import highdicom
from pydicom import dcmread
# Read a DICOM file and access its image data
dataset = dcmread('image.dcm')
dicom_image = highdicom.from_dicom(dataset)
frames = dicom_image.pixel_array- Whether pyjpegls is required for all use cases or only for JPEG-LS compressed images
- Performance characteristics when processing large multi-frame DICOM datasets
- Completeness of support for all DICOM modalities and derived object types
What it is and what it does
Highdicom is a pure Python library that sits on top of pydicom to simplify working with DICOM medical images in computational contexts. It handles three main workflows: reading DICOM files of various modalities (radiology, pathology, etc.) and preparing their frames for analysis, including spatial arrangement and pixel transforms that pydicom does not handle; writing derived DICOM objects such as annotations, segmentations, parametric maps, structured reports, and secondary captures; and reading and filtering those derived DICOM files to extract stored information.
The package targets machine learning, computer vision, and similar computational analyses where standard DICOM tooling leaves gaps. It depends on pydicom for core DICOM parsing, numpy and pillow for image handling, pyjpegls for JPEG-LS codec support, typing-extensions for type hints, and packaging for version management. It requires Python 3.10 or later and installs as a pure wheel with no compiled dependencies.
Use it for
- Prepare medical imaging datasets for machine learning by reading DICOM frames and applying standardized pixel transforms for computational analysis.
- Store machine learning model outputs (segmentations, heatmaps, predictions) back into DICOM format for clinical workflow integration and archival.
- Create structured DICOM reports containing numerical results and annotations from computational analyses or human review.
- Convert single-frame DICOM images to multi-frame format or apply presentation state transformations for standardized display.
- Extract and filter information from existing derived DICOM objects (segmentations, parametric maps) for downstream processing.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Highdicom is actively maintained, permissively licensed (MIT), has low install friction, and fills a genuine gap in DICOM tooling for computational workflows. It is well-supported by the imaging research community (NCI Imaging Data Commons, QIICR, Radiomics) and has no known security vulnerabilities. Install it if you work with DICOM files in machine learning or computational analysis contexts.
Install
highdicom on PyPI
Before you install
Low friction installation with pure Python distribution. Active maintenance with recent releases; last commit 2026-08-07. Supports current Python versions (3.10–3.14).
Requires Python 3.10 or later. JPEG-LS compressed images require pyjpegls, which may need system libraries depending on your platform.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for research and production deployments.
Quickstart
pip install highdicom
import highdicom
from pydicom import dcmread
# Read a DICOM file and access its image data
dataset = dcmread('image.dcm')
dicom_image = highdicom.from_dicom(dataset)
frames = dicom_image.pixel_arrayVerify before relying
- Whether pyjpegls is required for all use cases or only for JPEG-LS compressed images
- Performance characteristics when processing large multi-frame DICOM datasets
- Completeness of support for all DICOM modalities and derived object types
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesnumpypillowpydicompyjpeglstyping-extensionspackaging |
| Maintenance | Actively maintained 17 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 83,636 / month, #14,060 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Multimedia :: GraphicsTopic :: Scientific/Engineering :: Information Analysis |
Evidence: highdicom-0.28.1-py3-none-any.whl
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See also dicomweb-client · pydicom · dicom2nifti · python-gdcm · pylibjpeg · TotalSegmentator · presidio-image-redactor · openslide-python · torchxrayvision · pylibjpeg-libjpeg