Module Type & Category
Category: Base or (Filtering, Registration, etc.)
Authors, Collaborators & Contact
- Author1: Raul San Jose, BWH
- Contributor1: Alex Yarmakovich, Isomics
- Contact: Raul San Jose, lmi.bwh.harvard.edu/~rjosest
ROI Seeding is a tractography implementation that allows a user to seed tracts from a region of interest (ROI). The ROI is defined as a labelmap and has to be provided by the user. One approach to generate the ROI is by means of the Editor module.
Examples, Use Cases & Tutorials
- We want to study the white matter integrate across a population for a given tract. ROISeeding can be used to carry this study. First, a region of interest in the tract that is under study has to be defined, for example by manually delineating the ROI using the Editor module. Second, the DWI has to be loaded and the tensor has to be created using the [Diffusion Tensor Estimation | Modules:DiffusionTensorEstimation-Documentation-3.4]. Thrid, start the ROI Seeding using the DTI volume and the ROI labelmap as main volume inputs.
Quick Tour of Features and Use
The module is very straighforward to use:
- Input DTI volume:set the DTI volume that is going to be used for tractography
- Parameters panel:
- Output panel:
- Viewing panel:
Caveats and other limitations
ROISeeding module does not work when the labelmap dimensions do not match the DTI volume dimensions. The work-around is to resample labelmap using [Filtering -> Resample Volume 2 | http://www.slicer.org/slicerWiki/index.php/Modules:ResampleVolume2-Documentation-3.4].
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Source code & documentation
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