ohif/viewers

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

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Summary Information

Updated 50 minutes ago
Added to GitGenius on September 16th, 2026
Created on October 13th, 2015
Open Issues & Pull Requests: 556 (+0)
GitHub issues: Enabled
Number of forks: 4,341
Total Stargazers: 4,339 (+0)
Total Subscribers: 118 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.5 days
Mean response time: 29.6 days
90th percentile: 81.3 days
Tracked items: 871

Most active contributors

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How this project is maintained

Roughly one issue in three opened in the past year never receives a reply. 85% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "Regression" is answered fastest, typically in about 12 hours, while "PACS Enhancement" waits about 3 weeks. 73% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 43% of issues opened in the past year have been closed.

Charts & Analytics

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Issue Activity (beta)

Open issues: 406
New in 7 days: 5
Closed in 7 days: 1
Avg open age: 395 days
Stale 30+ days: 386
Stale 90+ days: 348

Recent activity

Opened in 7 days: 5
Closed in 7 days: 1
Comments in 7 days: 1
Events in 7 days: 1

Top labels

  • Awaiting Reproduction (314)
  • Bugs (277)
  • fixed-in-dev-await-release (48)
  • Feature (41)
  • Rendering Issues (30)
  • PACS Enhancement (23)
  • Awaiting Response (22)
  • Segmentation (16)

Detailed Description

OHIF Viewers is a zero-footprint DICOM viewer and oncology-specific Lesion Tracker built with shared extension packages.

The tool addresses the need to view and analyze medical imaging data without requiring local installation. It provides a web-based interface for examining DICOM images and includes specialized functionality for tracking lesions in oncology workflows. The architecture uses extension packages that can be composed and customized to support different imaging use cases and clinical requirements.

Developers should consider this tool for projects requiring browser-based medical image viewing, particularly those involving cancer imaging research or quantitative imaging analysis. The zero-footprint approach makes it suitable for deployment in healthcare environments where software installation is restricted. The Lesion Tracker component specifically targets oncology workflows where longitudinal tracking of lesions across imaging studies is clinically important. The extension-based architecture allows teams to build custom functionality on top of the core viewer rather than forking the entire codebase.

The project maintains active development with regular commits across its TypeScript codebase. Work spans both core viewer functionality and the extension system, indicating ongoing investment in the platform's architecture. The repository shows consistent engagement with issues and pull requests, suggesting responsive maintenance and community interaction.