1. Overview of pre-processing pipelines for VBM Their impact on spatial normalisation and statistical analysis Julio Acosta-Cabronero Department of Clinical Neurosciences University of Cambridge

2. PART I Skull Stripping

3. Skull Stripping Single Strategy Brain Extraction Tool v.1/2 (BET/BET2) Deformable model which evolves to fit the brain’s surface Brain Surface Extractor (BSE) Edge-based method that employs anisotropic diffusion filtering

4. Hybrid Watershed Algorithm (HWA) Combines watershed algorithms and deformable surface models Skull Stripping Hybrid Algorithms

5. HWA Using Atlas Information

6. Optimised BSE

7. Optimised BET

8. Optimised BET2

9. Default vs. Optimised Similarity (J) Optimal/Default SpecificityAutomation Proc Time BSE Default is unreliable, but a systematic method was found to obtain optimised skull-stripped volumes. 0.94/0.50 Very specific. It delineates accurately the brain boundary, but it may remove too much brain tissue. It usually excludes sinuses. Poor. Running only under Windows (Brainsuite2). Although optimised volumes can be obtained in a simple manner without need of visual inspection. 5 sec BET Default is reliable. Optimal f and g (maximising J) are within the ranges [0.4, 0.5] and [-0.1, 0], respectively. It was found empirically that reducing f to 0.4, undesired FN were avoided. 0.94/0.94 Good definition of brain boundary. More conservative than BSE. It usually includes bits of sinuses. Good. Fixing f to 0.4 (g=0) is a simple method to automate it reliably. Too conservative at times, but it does not usually remove brain tissue. 16 sec BET2Same as BET0.95/0.95Same as BET 27 sec HWA Optimisation by using atlas information slightly improved the output volumes. 0.84/0.83 Very conservative. It includes big chunks of CSF and sinuses Very good. No input parameters. 9 min

10. 1.- BET & BET2 are very similar, but BET2 seems to be slightly more accurate 2.- HWA is the most conservative method, but it ensures a very low FN rate and it does not require user intervention 3.- BET & BET2 could be automated in a conservative manner – f=0.4, g=0; although it does not ensures FN rate ~ 0 4.- BSE can be very useful if very specific skull-stripped volumes are required or if high-quality scans are used, otherwise it may undesirably remove essential brain tissue Skull Stripping Summary

11. PART II Bias Correction

12. Intensity Non-Uniformity (r.f. Bias) Correction Locally-Adaptive Methods Non-parametric Non-uniform Intensity Normalisation (N3) Iterative modelling of low-frequency spatial variations in the data to maximise high-frequency information in the intensity histogram of the corrected volume. Bias Field Corrector (BFC) It also utilises an approach based on normalisation of regional tissue intensity histograms to global values.

13. Optimised BET2 Optimised BET2 + N3

14. e RMS BSEBETBET2HWA N34.294.324.264.34 BFC4.605.675.517.34 Bias Correction Phantom Work 1.- N3 outperformed BFC 2.- N3 performs similarly for all skull-stripping methods

15. PART III Unified Segmentation

16. Unified Segmentation Phantom Work – Standard Space MethodJFN (%)FP (%) NN Full Volume0.849.08.3-0.7 HWA + N30.808.315.26.9 BSE + N30.906.44.4-2.0 BET2, f=0.4 + N30.865.59.53.9 BET2, f=0.5 + N30.894.86.92.1

17. PART IV Statistical Analysis

18. Statistical Analysis Artificial Lesion