MRI preprocessing and segmentation

1. MRI preprocessing and segmentation

2. Bias References

3. Segmentation References

4. Validation Segmentation pipeline Clarke, 1995

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

6. 1.1. Brain extraction MRI of head Intracranial volume Extracted brain

7. 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

8. 1.1. Brain extraction initial mask adjacent slice j mask of slice j Huh, 2002 challenge

9. 1.1. Brain extraction restoring truncated boundary

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

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

12. 1.2. Removal of field inhomogeneities Statistical methods: probabilistic, gaussian and mixture models of bias-field Polynomial methods: smooth polynomial fit to bias-field

13. 1.2. Removal of field inhomogeneities Polynomial method example: Milchenko, 2006

14. Milchenko, 2006

15. orig model bias result 1.2. Removal of field inhomogeneities Shattuck, 2001

16. 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)

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

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

19. 3. Segmentation T1 weighted, single intensity dual echo:T2, PD or T1, T2, PD weighted or T1 weighted with feature vector 3.1. Histogram based thresholding 3.2. Bayesian 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

21. 3.1. Histogram based thresholding WM GM Lcp crossing point of tangents Histogram of extracted, bias corrected brain in T1-weighted MRI L = g * Lcp (set g manually on 80 images) if I(x,y,z) < L then GM else WM Schnack, 2001

23. Population1 Population2 Population3 3.2. Bayesian segmentation GM WM (#of voxels/#ofallvoxels in the brain) (intensity) Hypothetical distributions

24. 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: setup graphs above from regional data GM, WM, CSF ratios from volumetric studies P(Tj ! I) = P(I ! Tj) . P(Tj) Ξ P(I ! Tk). P(Tk) k J,k=1,2,3: 1: CSF, 2: GM, 3:WM Decide on tissue type m if: P(Tm ! I) > P(Tj ! I) for all j Kovacevic, 2002

25. 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

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

27. Suckling, 1999

28. 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 15 classes Zavaljevski, 2000

29. 3.3. Maximum likelihood classifier Zavaljevski, 2000 Normal subject Stroke patient

30. 3.4. K-NN, K-Nearest neighbor classifier Hypothetical distribution T2 intensity T1 intensity - k is always odd, 1<k<15 (as k increases comput time increases) - given a point p find k closest samples known from before - decide on class m where m is the highest number of classes among these k samples

31. 3.4. K-NN classifier Uses 5 different contrast MRIs manual atlas labels atlas labels labels with linear reg. with non-lin reg. k=1 k=45 Vrooman, 2007

32. 3.5. ANN, MLP classifier for segmentation, M = 3, 3 classes :F MLP Architecture: 1 layer: linear contour >1 layers: complex contours countours are used for class separation W1 W3 feature vector transfer fcn: sigmoid

33. 3.5. ANN, MLP classifier Results This page is empty on purpose

34. 3.6. k-means classifier This classifier is not used much in segmentation, but explained here as an introduction to fuzzy c-means 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:

35. 3.7. fuzzy c-means (FCM) classifier k-means classifier FCM classifier U: membership row=each sample x col=each class minimized cost

36. If || U(k+1) - U(k)||< 3.7. fuzzy c-means (FCM) classifier Initialize U=[uij] matrix, U(0) At k-step: calculate the centers vectors C(k)=[cj] with U(k) initial iteration 8 iteration 37 Update U(k) , U(k+1)

37. 3.7. fuzzy c-means classifier Results

38. 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

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