View source for EMSegmenter-Tasks:Non-Human-Primate

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=Description=
Non Human Primate pipeline facilitates segmentation of single-channel (T1w) MRI of the vervet brain. The current segmentation pipeline task operates on the skull-stripped brain MRI. In our experience, skull-stripping of NHP brain can be performed by registration of a subject with manually defined ICC to the subject being analyzed. We recommend using [[Modules:BRAINSFit|BRAINSFit]] module for registering ICC template to the subject. In order to improve the robustness of the registration process we recommend prior bias correction using [[Modules:N4ITKBiasFieldCorrection-Documentation-3.6|N4ITKBiasFieldCorrection]] module. The resulting ICC may need to be manually adjusted using [[Modules:Editor-Documentation-3.6|Editor]] module. In the future, we plan to substitute this registration-based skull-stripping approach with the [http://www.na-mic.org/Wiki/index.php/2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration SPECTRE Slicer plugin] currently under development.

The pipeline is using unbiased atlas of vervet brain. The atlas construction process was facilitated by the unbiased template construction approach implemented in [http://www.picsl.upenn.edu/ANTS/ ANTS] software. The atlas construction is described in the paper [in preparation]. The T1w MR images used in the atlas construction were acquired from the population of 10 male vervets ~4 y.o. The high-level atlas construction approach is the following:
# scan of one subject (S1) was selected
# an estimate of GM/WM/CSF segmentation was obtained using k-means clustering, followed by manual editing as necessary
# subcortical structures (Caudate, Hippocampus and Putamen) were segmented manually
# average template was obtained using [http://www.picsl.upenn.edu/ANTS/templatetut/tutorial.php ANTS Template building approach]
# the inverses of the transforms produced in the previous step for each subjects were applied to the manual segmentation obtained in Step 2
# the segmentations obtained in the previous step were pixel-wise averaged for each structure to obtain probabilistic atlases

The current segmentation pipeline consist of the following steps:
* Step 1: Register the atlas to the MRI scan via [[Modules:BRAINSFit| BRAINSFit]] (Johnson et al 2007)
* Step 2: Compute intensity distribution (mean and variance) for each label by automatically sampling from the MR scan. The sampling for a specific label is constrained to the region that consists of voxels with high probability (top 95%) of being assigned to the label according to the aligned atlas.
* Step 3: Automatically segment the MRI scan into the structures of interest using [[Modules:EMSegmenter-3.6|EM Algorithm]]  (Pohl et al 2007)

=Anatomical Tree=

* Root
** Head
***  intracranial cavity (ICC)
**** White matter + Gray matter + CSF class (WGC)
***** grey matter (GM)
****** left Subcortical Gray Matter (lSGM)
******* left caudate (lCaudate)
******* left putamen (lPutamen)
******* left hippocampus (lHippocampus)
****** right Subcortical Gray Matter (rSGM)
******* right caudate (rCaudate)
******* right putamen (rPutamen)
******* right hippocampus (rHippocampus)
****** Cortical Gray Matter (CGM)
***** white matter (WM)
***** cerebrospinal fluid (CSF)
**** Brainstem
**** Cerebellum
*** background (BG)

=Atlas=

Image Dimension = 256 x 256 x 252 <br>
Image Spacing   = 0.4688 x 0.4688 x 0.50002

Images were acquired at Wake Forest University. Acquisition details for the images used in the atlas construction: Axial T1-weighted MR images were acquired on a 3T GE scanner with a circularly-polarized, single channel dedicated RF coil with an internal diameter of 18.4 cm (Litzcage, Doty Scientific, Columbia, SC), using a 3D SPGR sequence (TI 600ms, TE 3.276ms, TR 15.28ms; flip angle 15o; matrix 256x256; FOV 12cm; in-plane resolution 0.47mm; slice thickness 0.5mm).

{| class="wikitable"
|-
| [[Image:EMSegmenter_NHP_template_t1.png|210px]]
| [[Image:EMSegmenter_NHP_CSF.png|210px]]
| [[Image:EMSegmenter_NHP_GM.png|210px]]
| [[Image:EMSegmenter_NHP_WM.png|210px]]
|-
| Template (T1)
| CSF
| GM
| WM
|}

=Result=
<gallery perrow=1: widths=630px : heights=210px>
Image:EMSegmenter NHP T1.png
Image:NHP_Labelmap.png
</gallery>

=Acknowledgment=
The construction of the pipeline was supported by funding from NIH NCRR 2P41RR013218 Supplement and by R01 AA016748 [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_NCBC_Collaboration:Measuring_Alcohol_and_Stress_Interaction (NA-MIC NCBC Collaboration with Wake Forest University and Virginia Tech: Measuring Alcohol and Stress Interaction)].

=Collaborators=
* James Daunais (WFU)
* Chris Wyatt (VT)
* Bob Croft (WFU)
* Andriy Fedorov (BWH)
* Ron Kikinis (BWH)

=Citations=
* Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC N4ITK: Improved N3 Bias Correction, IEEE Trans Med Imag, 2010
* Pohl K, Bouix S, Nakamura M, Rohlfing T, McCarley R, Kikinis R, Grimson W, Shenton M, Wells W. [http://www.slicer.org/pages/Special:PubDB_View?dspaceid=608 A Hierarchical Algorithm for MR Brain Image Parcellation.] IEEE Transactions on Medical Imaging. 2007 Sept;26(9):1201-1212.
* S. Warfield, J. Rexilius, P. Huppi, T. Inder, E. Miller, W. Wells, G. Zientara, F. Jolesz, and R. Kikinis, “A binary entropy measure to assess nonrigid registration algorithms,” in MICCAI, LNCS, pp. 266–274, Springer, October 2001.
* Johnson H.J., Harris G., Williams K. [http://hdl.handle.net/1926/1291 BRAINSFit: Mutual Information Registrations of Whole-Brain 3D Images, Using the Insight Toolkit], The Insight Journal, July 2007
* A.Fedorov, X.Li, K.Pohl, S.Bouix, M.Styner, M.Addicott, C.Wyatt, J.B.Daunais, W.M.Wells, R.Kikinis "Atlas-guided Segmentation of Vervet Monkey Brain MRI". The Open Neuroimaging Journal, 2011, 5(Suppl 2-M7), pp.186-197. doi:10.2174/1874440001105010186 [http://www.na-mic.org/publications/item/view/2119 URL]