ali r. khan, mirza faisal beg - sfu.ca · ali r. khan, mirza faisal beg simon fraser university,...
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Whole brain image registration using multi-structure confidence-weighted anatomic constraintsAli R. Khan, Mirza Faisal BegSimon Fraser University, Canada
IntroductionInter-subject registration of whole brain magnetic resonance images
Challenging: high anatomical variability, convoluted folding of the cortexApplications: morphometry, functional localization, atlas creation
Our approach:Use automated Freesurfer [1] segmentations of multiple brain structures as simultaneous anatomic constraints (multi-structure registration), instead of as initialization [2]Weight these using trained segmentation confidence maps (SCMs)Large deformation diffemorphic framework for registration (LDDMM [3])
Methods
ResultsMulti-structure confidence-weighted LDDMM registration compared against:
Free-form Deformation B-splines, IRTK [4]Single channel LDDMM
1.5T brain MR scans brains from the Internet Brain Segmentation Repository (IBSR) [5]9 brains used for training SCMs, other 9 brains used for testing image registration
ConclusionsAnatomical constraints, in the form of automated segmentations, can improve brain registration
More accurate volumetry, morphometry, or functional localization in brain mapping studiesLimitations:
SCMs generated for subcortical structures only; future work will include cortical SCMsHigh computational cost with LDDMM; requires high-performance computing machines
References
[1] B. Fischl, et al., “Whole brain segmentation automated labeling of neuroanatomical structures in the human brain,” Neuron, vol. 33 (3), pp. 341–55, 2002.[2] A. R. Khan et al., “Freesurfer-initiated fully-automated subcortical brain segmentation in MRI using large deformation diffeomorphic metric mapping,” Neuroimage, vol. 41 (3), pp. 735–46, 2008.[3] M. F. Beg, et al., “Computing large deformation metric mappings via geodesic flows of diffeomorphisms,” International Journal of Computer Vision, vol. 61 (2), pp. 139–57, 2005.[4] Rueckert et al., “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Transactions on Medical Imaging, vol. 18 (8), pp. 712–21, 1999.[5] IBSR data was provided by the Center for Morphometric Analysis at Massachusetts General Hospital and is available at http://www.cma.mgh.harvard.edu/ibsr/.
Medical Image Analysis Lab, School of Engineering ScienceSimon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada
Ali R. Khan ([email protected]), Mirza Faisal Beg ([email protected]) Website: http://www.autobrainmapping.com
2. Train segmentation confidence maps for each brain structureFor each training subject:
a. Find local errors between manual and automated structureb. Spatially normalize these to the atlasc. Compute SCM as probability of accuracy
1. Run FreesurferSubcortical and cortical
Fully automated segmentation
Segmentation errors?Train confidence maps
3. Perform multi-structure confidence-weighted LDDMM registration to atlas16 subcortical, and 35 cortical structures used in the multi-structure registrationProvides anatomical constraints to help guide the high-dimensional registration
Single-channel Multi-structure
6.05.04.03.02.01.00.0
Surface Distance(mm)
Reference Single-channel Multi-structure
Cortical surface distances
Propagated cortical surfaces
WM
GM
Vent
Thal
Caud
Put
Pall
Hipp
Amyg
Nuc Acc
0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
0.53
0.66
0.73
0.71
0.81
0.79
0.86
0.83
0.78
0.79
0.51
0.57
0.65
0.72
0.8
0.75
0.85
0.8
0.74
0.74
0.48
0.54
0.65
0.67
0.77
0.73
0.84
0.8
0.73
0.73
Dice Similarity Coefficient
IRTKSingle channelMulti-structure
Subcortical overlap
Segmentations Error map
ManualFreesurfer
SCM
SCMs
!subjAtlas
Subject
Atlas