Documentation/Nightly/Modules/SegmentationAidedRegistration

From Slicer Wiki
Jump to: navigation, search
Home < Documentation < Nightly < Modules < SegmentationAidedRegistration

For the stable Slicer documentation, visit the 4.10 page.

Introduction and Acknowledgements

This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the NA-MIC website.

Contributor: Yi Gao, Brigham and Women's Hospital
Contributor: Josh Cates, University of Utah
Contributor: Liang-Jia Zhu, University of Alabama at Birmingham
Contributor: Alan Morris, University of Utah
Contributor: Danny Perry, University of Utah
Contributor: Greg Gardner, University of Utah
Contributor: Rob MacLeod, University Utah
Contributor: Sylvain Bouix, Brigham and Women's Hospital
Contributor: Allen Tannenbaum, University of Alabama at Birmingham
Contact: Yi Gao, <email>gaoyi@gatech.edu</email>

National Alliance for Medical Image Computing (NA-MIC)  
Psychiatry Neuroimaging Laboratory (PNL)  
The University of Alabama at Birmingham  
Scientific Computing and Imaging Institute (SCI)  

Module Description

When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.

Use Cases

  • In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.

Tutorials

  • Step 1. Give the input images, including:
    • Fixed grayscale image
    • Segmentation label image for the target region in the fixed image
    • Moving grayscale image
    • Segmentation label image for the target region in the moving image
  • Step 2. Give the output filename.
  • Run
  • There is an optional parameter:
    • Only perform deformable registration locally? Yes by default. The output registered grayscale image will only be around the target region. Uncheck to get the registered moving image in the entire region, but this is VERY SLOW.

Panels and their use

  • Input
    • Fixed grayscale image
    • Segmentation label image for the target region in the fixed image
    • Moving grayscale image
    • Segmentation label image for the target region in the moving image
  • Output
    • Registered moving image
  • Parameter (pptional)
    • Only perform deformable registration locally? Yes by default. The output registered grayscale image will only be around the target region. Uncheck to get the registered moving image in the entire region, but this is VERY SLOW.

Similar Modules

References

Information for Developers