Proposed revised atlas construction workflow

Objective

Develop an atlas construction workflow for vervet segmentation, which will maximally (completely) consist of the NA-MIC tools. Learn from the previous experience and try to mimic as closely as possible the workflow developed by Styner et al. [1]

Workflow / Tools

Workflow step	Description	Tools
1. Preparation	1.1. Select randomly a template image subject 1.2. Semi-automatically identify the ICC in the template subject. 1.3. 6+9+12 DOF registration of each of the subjects to the template. 1.4. Skull-strip each subject using dilated ICC of the template. 1.5. Perform intensity calibration to the template. 1.6. Use affine-registered, skull-stripped images to form initial affine average image.	1.1. n/a 1.2. Slicer Editor module 1.3. Slicer BRAINSFit extension 1.4. Slicer Mask, Editor modules (verify the mask encloses actual ICC!) 1.5. Slicer Histogram Matching module 1.6. Tool to be developed
2. Atlas construction	2.1. Use the affine registered image from Step 1.6 as the template to repeat Steps 1.1-1.6. 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. Slicer BRAINSFit extension (BSpline), possibly Groupwise BSpline registration by Balci et al. [2] 2.3. Tool from step 1.6.
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. 3.3. Back-propagate WM/GM/CSF segmentations to the training subjects using the deformation field from Step 2.2. 3.4. Construct the probabilistic atlas by averaging of the tissue maps, normalize locally to 1, construct rejection class by inverting the sum of WM+GM+CSF.	3.1. Slicer editor module (thresholding, manual editing) 3.2. Gaussian smoothing module (check availability) 3.3. Slicer applyDeformation module (command line accessible only!) 3.4. Tool from Step 1.6, tool to be developed, Slicer Editor module

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?

References

1. 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
2. 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