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MRI preprocessing and segmentation

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Bias References

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Segmentation References

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Segmentation pipeline Clarke, 1995 Validation

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1. Preprocessing 1.1. Brain extraction 1.2. Removal of field inhomogeneities (bias-field)

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1.1. Brain extraction MRI of head Intracranial volume Extracted brain

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1.1. Brain extraction FSL: Initiate a mesh inside the skull and expand-wrap onto brain surface Huh, 2002 method: go to mid sagittal, find brain, copy mask on adjacent slices correct the copied mask

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1.1. Brain extraction initial mask adjacent slice j mask of slice j challenge Huh, 2002

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1.1. Brain extraction restoring truncated boundary

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Let voxel have a value 1 if its intensity is higher than t (determine t arbitrarily, increase when needed)

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1.2. Removal of field inhomogeneities Bias field Phantom studies: Typical signal falloff in SI direction is 20% SISI 20 % x intensity

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1.2. Removal of field inhomogeneities Statistical methods: probabilistic, gaussian and mixture models of bias-field Polynomial methods: smooth polynomial fit to bias-field

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1.2. Removal of field inhomogeneities Polynomial method example: Milchenko, 2006

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1.2. Removal of field inhomogeneities Shattuck, 2001 orig model biasresult

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2. Feature extraction Features: - Intensities in a single MRI: univariate classification - Feature vector from a single MRI: multi-variate class. ex: [I(x,y,z) f(N(x,y,z)) g(N(x,y,z))] where N : neighbourhood around (x,y,z) f: distribution of I in neighborhood (entropy) g: average I in neighborhood or f, g specify edge or boundary information - Intensities in multiple MRIs with different contrast: multi-variate (multi-spectral)

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3. Segmentation 4 regions: R1: air, scalp, fat, skull (background, removed) R2: subarachnoid space (CSF) R3: parenchyma (GM, WM) R4: ventricles(CSF) 3 tissue types: CSF, GM, WM

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3. Segmentation Clarke, 1995 (T1 weighted) (dual echo:T2, PD or T1, T2, PD weighted)

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3. Segmentation T1 weighted, single intensitydual echo:T2, PD or T1, T2, PD weighted or T1 weighted with feature vector 3.1. Histogram based thresholding Unsupervised 3.6. k-means 3.7. fuzzy cmeans Supervised Parametric Non-parametric ANN 3.3. Max. Likelihood 3.4. k-NN 3.5. MLP 3.2. Bayesian

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3.1. Histogram based thresholding Schnack, 2001 WM GM Histogram of extracted, bias corrected brain in T1-weighted MRI L cp crossing point of tangents L = g * L cp (set g manually on 80 images) if I(x,y,z) < L then GM else WM

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Population1 Population2 Population Bayesian segmentation WM GM Hypothetical distributions (intensity) (#of voxels/#ofallvoxels in the brain)

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3.2. Bayes’ classifier For each voxel, x,y,z: Assume K tissue types (for eg. T1, T2,..., Tk) possible, for 1 observed intensity, I: P(Tj ! I) = P(I ! Tj). P(Tj) Ξ P(I ! Tk). P(Tk) k GM, WM, CSF ratios from volumetric studies setup graphs above from regional data Decide on tissue type m if: P(Tm ! I) > P(Tj ! I) for all j Kovacevic, 2002 J,k=1,2,3: 1: CSF, 2: GM, 3:WM

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Methods based on feature vector or multi-spectral data Supervised vs unsupervised Methods Supervised: - Color indicates known classes - Separation contour is to be found during training phase - Separation contour is used for classification during recall phase Unsupervised: - No color, classes unknown - Clusters are found during training phase - Association with clusters are made during recall phase

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Kovacevic, 2001 T2 weighted voxel x,y,z PD weighted image T2 weighted image intensity

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Suckling, 1999

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3.3. Maximum likelihood classifier - Assume the distribution P(I ! Tj) in Bayes can be obtained by a mixture of Gaussian or Normal distribution - Estimate means and co-variance matrix - For better results use Hidden Markov fields within neighborhoods Zavaljevski, classes

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3.3. Maximum likelihood classifier Zavaljevski, 2000 Normal subject Stroke patient

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3.4. K-NN, K-Nearest neighbor classifier T1 intensity T2 intensity Hypothetical distribution - k is always odd, 1

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3.4. K-NN classifier k=1 k=45 manual atlas labels atlas labels labels with linear reg. with non-lin reg. Vrooman, 2007 Uses 5 different contrast MRIs

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MLP Architecture: 1 layer: linear contour >1 layers: complex contours countours are used for class separation transfer fcn: sigmoid W1 W3 :F 3.5. ANN, MLP classifier for segmentation, M = 3, 3 classes feature vector

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3.5. ANN, MLP classifier Results This page is empty on purpose

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3.6. k-means classifier Algorithm: - k is equal to number of classes - choose k arbitrary initial seed points (*) - assume seed points are class centroids 1 for each sample point j, find distance to all k centroids Let j belong to class m if j is closest to centroid m 2 for each class k, recalculate centroids repeat steps 1 and 2 above until no change in centroids Note how class assignments change at each iteration Minimized measure: This classifier is not used much in segmentation, but explained here as an introduction to fuzzy c-means

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3.7. fuzzy c-means (FCM) classifier k-means classifierFCM classifier U: membership row=each sample x col=each class minimized cost

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3.7. fuzzy c-means (FCM) classifier initial iteration 8 iteration 37 Initialize U=[u ij ] matrix, U (0) At k-step: calculate the centers vectors C (k) =[c j ] with U (k) Update U (k), U (k+1) If || U (k+1) - U (k) ||< then STOP; otherwise return to step 2.

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3.7. fuzzy c-means classifier Results

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4. Validation Important issues: - Partial volume effect, visualization - Validation in manually segmented image - Performance comparison with other methods on simulated image: Ex: Brainweb from Mcgill

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4. Validation Partial volume effect for boundary separation Shattuck, 2001 corrrect WM misclassified (colored by subejct number there are a total of 10 subjects) segmented gold std Clark, 2006

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