Overview of pre-processing pipelines for VBM

Their impact on spatial normalisation and statistical

analysis

Julio Acosta-Cabronero

Department of Clinical NeurosciencesUniversity of Cambridge

PART I

Skull Stripping

Skull StrippingSingle 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

• Hybrid Watershed Algorithm (HWA)Combines watershed algorithms and deformable surface models

Skull StrippingHybrid Algorithms

HWA Using Atlas Information

Optimised BSE

Optimised BET

Optimised BET2

Default vs. OptimisedSimilarity (J)

Optimal/DefaultSpecificity Automation

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

BET2 Same as BET 0.95/0.95 Same as BET Same as BET 27 sec

HWA

Optimisation by using atlas information slightly improved the output volumes.

0.84/0.83Very conservative. It includes big chunks of CSF and sinuses

Very good. No input parameters.

9 min

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 StrippingSummary

PART II

Bias Correction

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.

Optimised BET2

Optimised BET2 + N3

eRMS BSE BET BET2 HWA

N3 4.29 4.32 4.26 4.34

BFC 4.60 5.67 5.51 7.34

Bias CorrectionPhantom Work

1.- N3 outperformed BFC

2.- N3 performs similarly for all skull-stripping methods

PART III

Unified Segmentation

Unified SegmentationPhantom Work – Standard Space

Method J FN (%) FP (%) N

Full Volume 0.84 9.0 8.3 -0.7

HWA + N3 0.80 8.3 15.2 6.9

BSE + N3 0.90 6.4 4.4 -2.0

BET2, f=0.4 + N3 0.86 5.5 9.5 3.9

BET2, f=0.5 + N3 0.89 4.8 6.9 2.1

PART IV

Statistical Analysis

Statistical AnalysisArtificial Lesion

Statistical AnalysisArtificial Lesion