DICOM Microscopy Viewer

Vanilla JS library for web-based visualization of DICOM VL Whole Slide Microscopy Image datasets.

The viewer allows visualization of slide microscopy images stored in a DICOMweb compatible archive. It leverages the dicomweb-client JavaScript library to retrieve data from the archive.

Features

• Display of different image types: VOLUME, OVERVIEW, LABEL
• Annotation of regions of interest (ROI) as vector graphics based on 3-dimensional spatial coordinates (SCOORD3D): POINT, MULTIPOINT, POLYLINE, POLYGON, ELLIPSE, ELLIPSOID
• Assembly of concatenations
• Decoding of compressed pixel data, supporting baseline JPEG, JPEG 2000 and JPEG-LS codecs
• Additive blending and coloring of monochromatic images of multiple optical paths (channels), supporting highly-multiplexed immunofluorescence imaging

Live demo

Check out the online examples at microscopy.dcmjs.org.

Documentation

The online Application Programming Interface (API) documentation is available at mghcomputationalpathology.github.io/dicom-microscopy-viewer.

Getting started

Take a look at the examples in the /examples directory. They are also available online at microscopy.dcmjs.org.

Basic usage

The viewer can be embedded in any website, one only needs to

• Create an instance of the viewer.VolumeViewer. The constructor requires an instance of DICOMwebClient for retrieving frames from the archive as well as the metadata for each DICOM image instance formatted according to the DICOM JSON Model.

• Call the render() method, passing it the HTML element (or the name of the element), which shall contain the viewport.

const url = 'https://localhost:8080/dicomweb';
const client = new DICOMwebClient.api.DICOMwebClient({url});
const studyInstanceUID = '1.2.3.4';
const seriesInstanceUID = '1.2.3.5';
const searchInstanceOptions = {
  studyInstanceUID,
  seriesInstanceUID
};
client.searchForInstances(searchInstanceOptions).then((instances) => {
  const promises = []
  for (let i = 0; i < instances.length; i++) {
    const sopInstanceUID = instances[i]["00080018"]["Value"][0];
    const retrieveInstanceOptions = {
      studyInstanceUID,
      seriesInstanceUID,
      sopInstanceUID,
    };
    const promise = client.retrieveInstanceMetadata(retrieveInstanceOptions).then(metadata => {
      const imageType = metadata[0]["00080008"]["Value"];
      if (imageType[2] === "VOLUME") {
        return(metadata[0]);
      }
    });
    promises.push(promise);
  }
  return(Promise.all(promises));
}).then(metadata => {
  metadata = metadata.filter(m => m);
  const viewer = new DICOMMicroscopyViewer.viewer.VolumeViewer({
    client,
    metadata
  });
  viewer.render({container: 'viewport'});
});

Citation

Please cite the following article when using the viewer for scientific studies: Herrmann et al. J Path Inform. 2018:

@article{jpathinform-2018-9-37,
    Author={
        Herrmann, M. D. and Clunie, D. A. and Fedorov A. and Doyle, S. W. and Pieper, S. and
        Klepeis, V. and Le, L. P. and Mutter, G. L. and Milstone, D. S. and Schultz, T. J. and
        Kikinis, R. and Kotecha, G. K. and Hwang, D. H. and Andriole, K, P. and Iafrate, A. J. and
        Brink, J. A. and Boland, G. W. and Dreyer, K. J. and Michalski, M. and
        Golden, J. A. and Louis, D. N. and Lennerz, J. K.
    },
    Title={Implementing the {DICOM} standard for digital pathology},
    Journal={Journal of Pathology Informatics},
    Year={2018},
    Number={1},
    Volume={9},
    Number={37}
}

Installation

Install the dicom-microscopy-viewer package using the npm package manager:

npm install dicom-microscopy-viewer

Building & Testing

Build code locally:

git clone https://github.com/mghcomputationalpathology/dicom-microscopy-viewer ~/dicom-microscopy-viewer
cd ~/dicom-microscopy-viewer
npm install
npm run build

test code locally:

git clone https://github.com/mghcomputationalpathology/dicom-microscopy-viewer ~/dicom-microscopy-viewer
cd ~/dicom-microscopy-viewer
npm install
npm test

We use rollup for bundling and Jest for testing.

Build the documentation:

npm run generateDocs

Support

The developers gratefully acknowledge their reseach support:

• Open Health Imaging Foundation (OHIF)
• Quantitative Image Informatics for Cancer Research (QIICR)
• Radiomics
• Imaging Data Commons (IDC)
• Neuroimage Analysis Center
• National Center for Image Guided Therapy
• MGH & BWH Center for Clinical Data Science (CCDS)