Projects:QIN:3D Slicer Annotation Image Markup

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Scope of Work

Annotation and Image Markup (AIM) project provides the foundation for enabling quantitative analysis of the results produced by the software tools by establishing the methodology to organize and describe the various representation of anatomical entities together with the semantic content and the image data. Unfortunately, the support of AIM in the key medical imaging research tools is currently lacking.

3D Slicer is a multi-platform free and open source software for visualization and medical image computing. NIH and NCI are major sponsors. 3D Slicer is currently central to the QIN grant activity at several of the QIN network sites and in the broader community. 3D Slicer currently includes support for rich set of annotations that can be created using 3D Slicer to support quantitative image analysis. However, these annotations are currently stored in a non-AIM format.

This activity will implement support of AIM in 3D Slicer, including storage of annotations produced by 3D Slicer in AIM format and importing AIM annotations into 3D Slicer. As a result, we will enable standardized storage and access to the results of quantitative analysis produced by the networked QIN grantees for improved analysis and biomarker validation based on the specific requirements and priorities determined by the QIN community.

Research Plan

Our implementation plan will be driven by the use-cases provided by the QIN community.

First, we will collect a collection of detailed use cases that utilize annotations and/or AIM. These use cases will provide specific examples to drive and test our implementation.

Second, based on the defined use cases, we will develop AIM import capability in 3D Slicer so that the annotations created using other tools (e.g., ClearCanvas and EPAD) can be loaded and displayed in 3D Slicer.

Third, we will implement functionality to save the annotations created in 3D Slicer into AIM format.

The compatibility of the implementation will be tested using the QIN-defined use cases and the existing tools that support AIM functionality.

Progress

  • Dec 2, 2011: RSNA2011: meeting with Pat Mongolwat, Vlad Kleper, Larry Tarbox. Discussed C++ API for AIM v.3, currently available on Windows. ClearCanvas can save annotations in either DICOM SR or AIM. AIM can be converted into DICOM SR using a standalone tool distributed with AIM API Windows libraries. DICOM SR can be loaded from file. Discussed ideas for implementation:
  1. use C++ AIM API (this is Win only for now)
  2. write XML directly (may not be compatible with other AIM versions)
  3. convert MRML into AIM XML (there is no MRML schema right now)
  • Nov 25, 2011: ClearCanvas workstation v 3.0.3 was installed and tested. Justin Kirby (NCI) provided first complete use case (breast MRI bi-dimensional tumor measurement). ClearCanvas can save annotations locally on disk, and can push to the AIM Data Service server, but cannot load them from file. See instructions below.
  • Nov 7, 2011: AIM API TCON with the QIN participants and BWH team.

Existing AIM-compatible Tools

ClearCanvas

Using AIM annotations with ClearCanvas, as explained by Justin Kirby.

Prerequisites:

  • Windows
  • Admin account (otherwise DICOM studies cannot be imported apparently)
  • Cannot query AIM data service while on Partners wireless, need cable connection

Steps to load images and annotations into ClearCanvas:

  1. Explorer -> DICOM, select folder with the studies of interest, click 'Import'
  2. In the tab with the selected study, click "AIM Data Service" and enter URL listed above
  3. Back to Explorer -> AIM Data Service, click "Search"
  4. Locate annotation that corresponds to the selected study, right mouse click, "Retrieve Selected Annotations"
  5. Back to the selected study tab, should be able to see annotations if select "Show all AIM annotations"

AIM ePAD

Resources

Web

Bibliography

  • Rubin, D. L., Mongkolwat, P., Kleper, V., Supekar, K., & Channin, D. S. (2009). Annotation and Image Markup: Accessing and Interoperating with the Semantic Content in Medical Imaging. IEEE Intelligent Systems, 24(1), 57-65. doi:10.1109/MIS.2009.3 IEEE Explore
  • Channin, D. S., Mongkolwat, P., Kleper, V., Sepukar, K., & Rubin, D. L. (2010). The caBIG annotation and image Markup project. Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology, 23(2), 217-25. doi:10.1007/s10278-009-9193-9 Pubmed
  • Clunie, D. A. (2007). DICOM Structured Reporting and Cancer Clinical Trials Results. Cancer Informatics, 4, 33-56. Libertas Academica. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21469002
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