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1、Voxel-Based Morphometry with Unified Segmentation,Ged Ridgway University College London,Thanks to: John Ashburner and the FIL Methods Group.,Preprocessing in SPM,Realignment With non-linear unwarping for EPI fMRI Slice-time correction Coregistration Normalisation Segmentation Smoothing,SPM8s unified
2、 tissue segmentation and spatial normalisation procedure,But first, a brief introduction to Computational Neuroanatomy,Aims of computational neuroanatomy,Many interesting and clinically important questions might relate to the shape or local size of regions of the brain For example, whether (and wher
3、e) local patterns of brain morphometry help to: Distinguish schizophrenics from healthy controls Understand plasticity, e.g. when learning new skills Explain the changes seen in development and aging Differentiate degenerative disease from healthy aging Evaluate subjects on drug treatments versus pl
4、acebo,Alzheimers Disease example,Baseline Image Standard clinical MRI 1.5T T1 SPGR 1x1x1.5mm voxels,Repeat image 12 month follow-up rigidly registered,Subtraction image,SPM for group fMRI,fMRI time-series,Preprocessing,Stat. modelling,spm T Image,Results query,Group-wise statistics,“Contrast” Image,
5、SPM for structural MRI,High-res T1 MRI,Group-wise statistics,?,?,?,?,High-res T1 MRI,High-res T1 MRI,The need for tissue segmentation,High-resolution MRI reveals fine structural detail in the brain, but not all of it reliable or interesting Noise, intensity-inhomogeneity, vasculature, MR Intensity i
6、s usually not quantitatively meaningful (in the same way that e.g. CT is) fMRI time-series allow signal changes to be analysed statistically, compared to baseline or global values Regional volumes of the three main tissue types: gray matter, white matter and CSF, are well-defined and potentially ver
7、y interesting,Examples of segmentation,GM and WM segmentations overlaid on original images,Structural image, GM and WM segments, and brain-mask (sum of GM and WM),Segmentation basic approach,Intensities are modelled by a Gaussian Mixture Model (AKA Mixture Of Gaussians) With a specified number of co
8、mponents Parameterised by means, variances and mixing proportions (prior probabilities for components),Non-Gaussian Intensity Distributions,Multiple MoG components per tissue class allow non-Gaussian distributions to be modelled E.g. accounting for partial volume effects Or possibility of deep GM di
9、ffering from cortical GM,Tissue Probability Maps,Tissue probability maps (TPMs) can be used to provide a spatially varying prior distribution, which is tuned by the mixing proportions These TPMs come from the segmented images of many subjects, done by the ICBM project,Class priors,The probability of
10、 class k at voxel i, given weights is then: Where bij is the value of the jth TPM at voxel i.,Aligning the tissue probability maps,Initially affine-registered using a multi-dimensional form of mutual information Iteratively warped to improve the fit of the unified segmentation model to the data Fami
11、liar DCT-basis function concept, as used in normalisation,MRI Bias Correction,MR Images are corupted by smoothly varying intensity inhomogeneity caused by magnetic field imperfections and subject-field interactions Would make intensity distribution spatially variable A smooth intensity correction ca
12、n be modelled by a linear combination of DCT basis functions,Summary of the unified model,SPM8 implements a generative model Principled Bayesian probabilistic formulation Combines deformable tissue probability maps with Gaussian mixture model segmentation The inverse of the transformation that align
13、s the TPMs can be used to normalise the original image Bias correction is included within the model,Segmentation clean-up,Results may contain some non-brain tissue (dura, scalp, etc.) This can be removed automatically using simple morphological filtering operations Erosion Conditional dilation,Lower
14、 segmentations have been cleaned up,The new segmentation toolbox,An extended work-in-progress algorithm Multi-spectral New TPMs including different tissues Reduces problems in non-brain tissue New more flexible warping of TPMs More precise and more “sharp/contrasty” results,New Segmentation TPMs,Seg
15、ment button,New Seg Toolbox,New Segmentation registration,Segment button,9*10*9 * 3 = 2430,New Seg Toolbox,59*70*59 * 3 = 731010,New Segmentation results,Segment button,New Seg Toolbox,Limitations of the current model,Assumes that the brain consists of only the tissues modelled by the TPMs No allowa
16、nce for lesions (stroke, tumours, etc) Prior probability model is based on relatively young and healthy brains Less appropriate for subjects outside this population Needs reasonable quality images to work with No severe artefacts Good separation of intensities Good initial alignment with TPMs.,Possible future extensions,Deeper Bayesian philosophy E.g. priors over means and variances Marginalisation of nuisance variables Model comparison, e.g. for numbers of Gaussians Groupwise mo
