Difference between revisions of "Initial atlas construction workflow"
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1.2. Manually identify the ICC in the template subject. | 1.2. Manually identify the ICC in the template subject. | ||
| − | 1.3. Perform intensity calibration to the template. | + | 1.3. Perform intensity calibration for all other subjects to the template. |
1.4. 12 DOF registration of each of the subjects to the template. | 1.4. 12 DOF registration of each of the subjects to the template. | ||
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| − | 2.1. Use the | + | 2.1. Use the skullstriped image from Step 1.2 as the template to repeat Steps 1.3-1.4. |
2.2. Perform non-rigid alignment of the subjects affinely registered to the template. | 2.2. Perform non-rigid alignment of the subjects affinely registered to the template. | ||
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2.1 see above | 2.1 see above | ||
| − | 2.2. | + | 2.2. Diffeomorphic demons |
| − | 2.3. | + | 2.3. Some tool in Slicer under develop |
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3.2. Slightly smooth the segmentations Styner et al. recommend kernel of 0.4 mm variance. | 3.2. Slightly smooth the segmentations Styner et al. recommend kernel of 0.4 mm variance. | ||
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| − | 3.1. | + | 3.1. Kmeans + manual |
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| − | 3. | + | 3.2. Gaussian smoothing module in Slicer, 0.6 and 0.65 kernel size used for Valentino and Tommy, respectively. |
| − | + | We did not perform back propagation due to the large structural differences in the dataset. | |
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Revision as of 20:34, 5 October 2009
Home < Initial atlas construction workflowContent
This page describes the current working pipeline of atlas construction for vervet data.
Data
10 vervet subjects (2 subjects possibly have unacceptable image quality), T1 sequence
Workflow / Tools
| Workflow step | Description | Tools |
|---|---|---|
| 1. Preparation |
1.1. Select the subject that is best oriented as the template image 1.2. Manually identify the ICC in the template subject. 1.3. Perform intensity calibration for all other subjects to the template. 1.4. 12 DOF registration of each of the subjects to the template. 1.5. Skull-strip each subject using dilated ICC of the template. |
1.1. n/a 1.2. manual in SNAP. 1.3. Slicer Histogram Matching module 1.4. FSL/Flirt 1.5. Slicer Mask, Editor modules (verify the mask encloses actual ICC!) |
| 2. Atlas construction |
2.1. Use the skullstriped image from Step 1.2 as the template to repeat Steps 1.3-1.4. 2.2. Perform non-rigid alignment of the subjects affinely registered to the template. 2.3. Compute the atlas as the average. |
2.1 see above 2.2. Diffeomorphic demons 2.3. Some tool in Slicer under develop |
| 3. Probabilistic atlas construction |
3.1. Segment WM/GM/CSF from the averaged template, manually edit to ensure accuracy 3.2. Slightly smooth the segmentations Styner et al. recommend kernel of 0.4 mm variance. |
3.1. Kmeans + manual 3.2. Gaussian smoothing module in Slicer, 0.6 and 0.65 kernel size used for Valentino and Tommy, respectively. We did not perform back propagation due to the large structural differences in the dataset. |
Standing questions
- when to perform bias correction? After Step 1.4?
- deep brain structures -- similar approach for identification in subjects as for GM/WM/CSF in Step 3.1?
- Styner et al. used T2 to segment CSF. We only have T1
References
- M. Styner, R. Knickmeyer, S. Joshi, C. Coe, S. J. Short, and J. Gilmore. Automatic brain segmentation in rhesus monkeys. Proc SPIE Medical Imaging Conference, Proc SPIE Vol 6512 Medical Imaging 2007, pp 65122L-1 - 65122L-8 pdf
- Balci S.K., Golland P., Wells W.M. Non-rigid Groupwise Registration using B-Spline Deformation Model. Insight Journal - 2007 MICCAI Open Science Workshop. link
- Previous descriptions of the atlas construction workflow: Summary by Ginger Li, Description off BSL atlas page
