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All Projects → cylab-tw → bluelight

cylab-tw / bluelight

Licence: MIT License
a browser-based DICOM viewer

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html5dicomdicomwebbluelightdicom-serverwhole-slide-imagingdicom-viewerorthancwsirtssdicom-web-viewerdicom-overlay

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BlueLight Web-based DICOM Viewer (BlueLight Viewer)

Blue Light is a browser-based medical image viewer is primarily maintained by the Imaging Informatics Labs. It is a pure single-page application (SPA), lightweight, and using only JavaScript and HTML5 technologies so as to deploy it on any HTTP server easily (just put it in HTTP server). It supports not only opening local data, but also connecting to medical image archives which support DICOMweb. It can display the various image markups and annotations such as Annotation and Image Markup (AIM), DICOM-RT structure set (RTSS), DICOM Overlay, and DICOM Presentation State. It provides tools for medical image interpretation and 3D image reconstruction, e.g., Multiplanar Rreformation or Reconstruction (MPR) and Volume Rendering (VR).

Live DEMO    Online Viewer    Video - Basic operation

Install

  • Put all files into any directory in the static directory on any HTTP server.

DICOMWeb Configuration

  • go to ./bluelight/data/config.json and change the configuration of DICOM server.
  • Reminder the DICOMWeb Plugin of the DICOM server shall be installed first.

About

  • BlueLight是少數能在網頁上顯示3D VR、MIP及MPR的開源DICOM瀏覽系統，它擁有平易近人的操作介面並支援RWD及Web零足跡瀏覽，可在任意大小的裝置上執行。
  • 標記顯示方面支援RTSS、Overlay、Graphic Annotation、AIM等標記，亦可於3D系統中轉換成3D標記。
  • 此專案亦支援LabelImg格式的標記繪製。
  • 3D VR顯示模式支援染色、窗度、透明、壓縮、貼皮、內插、降噪、打光、挖洞及最大密度投影，針對骨骼及肺氣管有專門的顯示模式，MPR模式則支援內插、貼皮、染色以及3D切面的顯示。
  • 通過台灣醫學資訊聯測 MI-TW 2020 - Track 4: DICOMWeb Query/Retrieve Consumer

Key Features

Network support

  • Load local files
  • Integration with any DICOMWeb Image Archive, including Raccoon, Orthanc, and dcm4chee server
    • Retrieve methods: WADO-URI (as default) and WADO-RS: specify one of them in config.json. - ContentType= application/dicom
  • Integration with IHE Invoke Image Display (IID) Profile, as the Image Display Acotr in Transaction [RAD-106]. (on going)
    • We are trying to implement it with in the scenario of FHIR ImagingStudy.
  • Integration with XNAT (currently doesn't build as an XNAT plugin)

2D image interpretation

  • Pan, zoom, move
  • Scroll images within a series
  • Rotation, Flip, Invert
  • Windowing
  • Cine
  • viewports: 4×4
  • Cross-Studies synchronization
  • Magnifier, etc
  • Line and angle measurement
  • hide/display markups and annotations
  • Export image

supported the display of the kinds of markups and annotations

  • GSPS: DICOM Graphic Annotation
  • DICOM Overlay
  • DICOM-RT structure set (RTSS)
  • Annotation and Image Markup (AIM)
  • DICOM SEG (Segementation)
  • LabelImg

3D Post-Processing

  • MPR (Multiplanar Reconstruction)
  • 3D Volume Rendering
  • MIP (maximum intensity projection)

Labeling tool interfaces (on experiment state)

  • LabelImg
  • GSPS: DICOM Graphic Annotation
  • DICOM-RT structure set (RTSS)
  • DICOM Overlay
  • DICOM SEG (Segementation)
    • Download as DCMTK DICOM-XML: only launch BlueLight
    • Download as DIOCM SEG: It is integrated with Raccoon.net. Please put the BlightLight on Raccoon.
  • Provide the function to convert the DICOM Overalys to a DICOM SEG object.

Supported library

Roadmap

  • FHIR ImagingStudy Query/Retrieve Interface
  • Support the IHE Invoke Image Display (IID) Profile [RAD-106]
  • Display DICOM Whole Slide Imaging (WSI) implemented by DICOMWeb. Referenced standard: DICOM WSI
  • Display DICOM Supplement 222 - Microscopy Bulk Simple Annotations Storage SOP Class. Referenced standard: DICOM Sup 219
  • Display DICOM Supplement 219 - JSON Representation of DICOM Structured Reports. Referenced standard: DICOM Sup 219
  • Display DICOM Structured Report
  • Display DICOM Waveform - 12 Lead ECG Waveform

Special projects

  • BlueLight-WSI: 🚧
  • BlueLight@Orthanc: ㊙️
  • BlueLight@XANT: ✅
  • [email protected]: ✅ - Raccoon.net is a noSQL-based medical image repository.

Acknowledgement

  • This project was supported by a grant from the Ministry of Science and Technology Taiwan.
  • We acknowledge AI99 teams at Taipei Veterans General Hospital (TVGH) for validation and provides many useful suggestions in many aspects of the clinical domain, especially to thank Dr. Ying-Chou Sun and his professional team.
  • Thanks 琦雯Queenie, Queenie's github for contributing the logo design.
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