# OHIF/Viewers

OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages

Repository: https://github.com/OHIF/Viewers
Canonical: https://ross.abutalabs.com/products/viewers
Homepage: https://docs.ohif.org/
Language: TypeScript
License: MIT
License Family: permissive
Topics: nci-itcr, nci-qin, quantitative-imaging, imaging-informatics, cancer-imaging-research, dicom, image-analysis, medical-image-processing, medical-imaging, reactjs, javascript, hacktoberfest, dicom-viewer, medical, imaging, healthcare-imaging
Last push: 2026-08-26T19:32:19+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 96, longevity 100
- inputs: {"age_days": 3977, "days_push": 7, "days_rel": 30, "gap_med": 12, "n_releases_24m": 24}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4309, forks 4327 (observed 2026-08-28T04:08:41.753840+00:00)

## What it is
OHIF Viewer is an open-source, zero-footprint web-based medical imaging viewer for DICOM images, built as a configurable and extensible progressive web application with React and Cornerstone3D. It works out-of-the-box with DICOMweb-compatible image archives and includes an oncology-focused Lesion Tracker plus shared extension packages for building custom imaging applications.

## Use cases
- view DICOM medical images in a web browser
- deploy a zero-footprint radiology viewer for a PACS
- track lesions in oncology imaging studies
- build a custom medical imaging application on an extensible framework
- view DICOM SEG segmentations and RT structure sets
- stream large radiology studies via DICOMweb
- render PET/CT fusion and volume rendering in the browser

## When to choose
- you need a web-based DICOM viewer that requires no local installation
- your image archive supports DICOMweb (or you can adapt via the Data Source API)
- you want an extensible, plugin-based imaging platform to build specialized workflows on
- you need features like measurement tracking, segmentation display, fusion, or volume rendering

## When to avoid
- you need FDA 510(k) cleared or CE marked diagnostic software for clinical diagnosis
- you need a native desktop PACS workstation rather than a browser-based viewer
- your images are not DICOM and you cannot adapt them via the Data Source API

## Facets
- artifact type: application
- maturity: active
- function: image-processing, ui-components, frontend-framework, plugin-system, web-framework
- domain: healthcare, image-processing, web-development, frontend, developer-tools
- platform: browser, self-hosted, cross-platform
- tags: dicom, dicomweb, medical-imaging, radiology, pacs, cornerstone3d, lesion-tracking, oncology, progressive-web-app, react, typescript, web-server, nodejs, docker

## Member repositories
- OHIF/Viewers (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:41.753840+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:48.024055+00:00, confidence not recorded.
  - readme: https://github.com/OHIF/Viewers (fetched 2026-08-28T04:08:41.753840+00:00, sha 7ed18ae3ffff)
  - homepage: https://docs.ohif.org/ (fetched 2026-08-29T09:11:17.430058+00:00, sha 14ec2e805f03)
  - site_page: https://docs.ohif.org/development/getting-started (fetched 2026-08-29T09:11:17.439262+00:00, sha 2946a4e1b1db)
  - site_page: https://docs.ohif.org/faq (fetched 2026-08-29T09:11:17.441200+00:00, sha abc83bead6cc)
- Data as of 2026-08-30T08:39:29.467469+00:00.