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OHIF Medical Imaging Viewer

The OHIF Viewer is a zero-footprint medical image viewer provided by the Open Health Imaging Foundation (OHIF). It is a configurable and extensible progressive web application with out-of-the-box support for image archives which support DICOMweb.

NPM version NPM downloads Pulls MIT License

Netlify Status CircleCI codecov All Contributors

What?

The OHIF Medical Imaging Viewer is for viewing medical images. It can retrieve and load images from most sources and formats; render sets in 2D, 3D, and reconstructed representations; allows for the manipulation, annotation, and serialization of observations; supports internationalization, OpenID Connect, offline use, hotkeys, and many more features.

Almost everything offers some degree of customization and configuration. If it doesn't support something you need, we accept pull requests and have an ever improving Extension System.

Why Choose Us

Community & Experience

The OHIF Viewer is a collaborative effort that has served as the basis for many active, production, and FDA Cleared medical imaging viewers. It benefits from our extensive community's collective experience, and from the sponsored contributions of individuals, research groups, and commercial organizations.

Built to Adapt

...

Support

...

Quick Start Deployment

This is only one of many ways to configure and deploy the OHIF Viewer. To learn more about your options, and how to choose the best one for your requirements, check out our deployment recipes and documentation.

The fastest and easiest way to get started is to include the OHIF Viewer with a script tag. In practice, this is as simple as:

  • Including the following dependencies with script tags:
  • Have an element with an ID of root on the page
  • Configure the OHIF Viewer at window.config:
window.config = {
  routerBasename: "/",
  servers: {
    dicomWeb: [
      {
        name: "DCM4CHEE",
        qidoRoot: "https://server.dcmjs.org/dcm4chee-arc/aets/DCM4CHEE/rs",
        wadoRoot: "https://server.dcmjs.org/dcm4chee-arc/aets/DCM4CHEE/rs",
        qidoSupportsIncludeField: true,
        imageRendering: "wadors",
        thumbnailRendering: "wadors"
      }
    ]
  }
};
  • Install the viewer: window.OHIFStandaloneViewer.installViewer(window.config);

This exact setup is demonstrated in this CodeSandbox and in our Embedding The Viewer deployment recipe.

Developing

Requirements

  • Yarn 1.17.3+
  • Node 8+
  • Yarn Workspaces should be enabled on your machine:
    • yarn config set workspaces-experimental true

Getting Started

  1. Fork this repository
  2. Clone your forked repository
    • git clone https://github.com/YOUR-USERNAME/Viewers.git
  3. Navigate to the cloned project's directory
  4. Add this repo as a remote named upstream
    • git remote add upstream https://github.com/OHIF/Viewers.git
  5. yarn install to restore dependencies and link projects

To Develop

From this repository's root directory:

# Enable Yarn Workspaces
yarn config set workspaces-experimental true

# Restore dependencies
yarn install

Commands

These commands are available from the root directory. Each project directory also supports a number of commands that can be found in their respective README.md and project.json files.

Yarn Commands Description
Develop
dev or start Default development experience for Viewer
dev:project <package-name> Replace with core, ui, i18n, cornerstone, vtk, etc.
test:unit Jest multi-project test runner; overall coverage
Deploy
build* Builds production output for our PWA Viewer
build:package* Builds production commonjs output for our Viewer
build:package-all* Builds commonjs bundles for all projects

* - For more information on our different builds, check out our Deploy Docs

Projects

The OHIF Medical Image Viewing Platform is maintained as a monorepo. This means that this repository, instead of containing a single project, contains many projects. If you explore our project structure, you'll see the following:

.
├── extensions              #
│   ├── _example            # Skeleton of example extension
│   ├── cornerstone         # 2D images w/ Cornerstone.js
│   ├── dicom-html          # Structured Reports as HTML in viewport
│   ├── dicom-microscopy    # Whole slide microscopy viewing
│   ├── dicom-pdf           # View DICOM wrapped PDFs in viewport
│   └── vtk                 # MPR and Volume support w/ VTK.js
│
├── platform                #
│   ├── core                # Business Logic
│   ├── i18n                # Internationalization Support
│   ├── ui                  # React component library
│   └── viewer              # Connects platform and extension projects
│
├── ...                     # misc. shared configuration
├── lerna.json              # MonoRepo (Lerna) settings
├── package.json            # Shared devDependencies and commands
└── README.md               # This file

Want to better understand why and how we've structured this repository? Read more about it in our Architecture Documentation.

Platform

These projects comprise the

Name Description Links
@ohif/core NPM
@ohif/i18n NPM
@ohif/viewer NPM
@ohif/ui NPM

Extensions

This is a list of Extensions maintained by the OHIF Core team. It's possible to customize and configure these extensions, and you can even create your own. You can read more about extensions here.

Name Description Links
@ohif/extension-cornestone NPM
@ohif/extension-dicom-html NPM
@ohif/extension-dicom-microscopy NPM
@ohif/extension-dicom-pdf NPM
@ohif/extension-vtk NPM

Acknowledgments

To acknowledge the OHIF Viewer in an academic publication, please cite

LesionTracker: Extensible Open-Source Zero-Footprint Web Viewer for Cancer Imaging Research and Clinical Trials

Trinity Urban, Erik Ziegler, Rob Lewis, Chris Hafey, Cheryl Sadow, Annick D. Van den Abbeele and Gordon J. Harris

Cancer Research, November 1 2017 (77) (21) e119-e122 DOI: 10.1158/0008-5472.CAN-17-0334

Note: If you use or find this repository helpful, please take the time to star this repository on Github. This is an easy way for us to assess adoption and it can help us obtain future funding for the project.

This work is supported primarily by the National Institutes of Health, National Cancer Institute, Informatics Technology for Cancer Research (ITCR) program, under a grant to Dr. Gordon Harris at Massachusetts General Hospital (U24 CA199460).

License

MIT © OHIF