Hi Salem, there are a couple extensions for 3D Slicer: SkullStripper and SwissSkullStripper. You can also use SPM's segmentation tool called 'new segment' to perform skull stripping. It will provide 4 tissue classes: wm, gm, csf, and "everything else". You can use the latter to perform skull stripping. There are probably many other options out there...these are only a few that came to my mind...hope this helps...

Thanks Michele for the suggestions, I'll check  them out !

Salut Salem!

Check out the latest pincram. Plos One paper and web link below. Let me know if I can help you get it up and running.

Rolf

http://soundray.org/pincram

Article Brain Extraction Using Label Propagation and Group Agreement: Pincram

Background:

X-ray computed tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although

CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing

step in brain imaging research is brain extraction

— the process of separating the brain tissue from all

other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated

and informally proposed, but never formally validated.

Aim:

To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head

CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard

with the results of several versions of automatic brain extraction and by estimating the reliability

of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data

smoothing is studied and reported.

Methods:

All images were thresholded using a 0–100 Hounsfield unit (HU) range. In one variant of the

pipeline, data were smoothed using a 3-dimensional Gaussian kernel (

σ = 1 mm3) and re-thresholded

to 0

–100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity

(FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were

filled.

For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were

selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue

plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually

created by one expert CT reader. The resulting brain tissuemasks were quantitatively compared to the

manual segmentations using sensitivity, speci

ficity, accuracy, and the Dice Similarity Index (DSI). Brain extraction

performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank

test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in

the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing

manual and automated segmentation:

ICVautomated

ICV

manual

.

To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines

for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV

of each scan was estimated and an intraclass correlation (ICC) was estimated using a one-way ANOVA.

Results:

Smoothing images improves brain extraction results using BET for all measures except specificity (all

p

b 0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35.

Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher

median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014,

p b 0.001), accuracy (0.9971 vs.

0.9971;median difference: 0.0001,

p b 0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p b 0.001)

and lower speci

ficity (0.9981 vs. 0.9982; median difference: −0.0001, p b 0.001). These measures are all very

high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using

smoothed data and an FI of 0.01, themean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates

the ICV estimates are similar for automated and manual segmentation.

In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929,

95% CI: 0.91, 0.945) for successfully extracted brains.

Background:

X-ray computed tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although

CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing

step in brain imaging research is brain extraction

— the process of separating the brain tissue from all

other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated

and informally proposed, but never formally validated.

Aim:

To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head

CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard

with the results of several versions of automatic brain extraction and by estimating the reliability

of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data

smoothing is studied and reported.

Methods:

All images were thresholded using a 0–100 Hounsfield unit (HU) range. In one variant of the

pipeline, data were smoothed using a 3-dimensional Gaussian kernel (

σ = 1 mm3) and re-thresholded

to 0

–100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity

(FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were

filled.

For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were

selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue

plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually

created by one expert CT reader. The resulting brain tissuemasks were quantitatively compared to the

manual segmentations using sensitivity, speci

ficity, accuracy, and the Dice Similarity Index (DSI). Brain extraction

performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank

test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in

the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing

manual and automated segmentation:

ICVautomated

ICV

manual

.

To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines

for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV

of each scan was estimated and an intraclass correlation (ICC) was estimated using a one-way ANOVA.

Results:

Smoothing images improves brain extraction results using BET for all measures except specificity (all

p

b 0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35.

Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher

median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014,

p b 0.001), accuracy (0.9971 vs.

0.9971;median difference: 0.0001,

p b 0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p b 0.001)

and lower speci

ficity (0.9981 vs. 0.9982; median difference: −0.0001, p b 0.001). These measures are all very

high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using

smoothed data and an FI of 0.01, themean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates

the ICV estimates are similar for automated and manual segmentation.

In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929,

95% CI: 0.91, 0.945) for successfully extracted brains.

X-ray computed tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although

CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing

step in brain imaging research is brain extraction

— the process of separating the brain tissue from all

other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated

and informally proposed, but never formally validated.

Aim:

To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head

CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard

with the results of several versions of automatic brain extraction and by estimating the reliability

of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data

smoothing is studied and reported.

Methods:

All images were thresholded using a 0–100 Hounsfield unit (HU) range. In one variant of the

pipeline, data were smoothed using a 3-dimensional Gaussian kernel (

σ = 1 mm3) and re-thresholded

to 0

–100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity

(FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were

filled.

For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were

selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue

plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually

created by one expert CT reader. The resulting brain tissuemasks were quantitatively compared to the

manual segmentations using sensitivity, speci

ficity, accuracy, and the Dice Similarity Index (DSI). Brain extraction

performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank

test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in

the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing

manual and automated segmentation:

ICVautomated

ICV

manual

.

To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines

for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV

of each scan was estimated and an intraclass correlation (ICC) was estimated using a one-way ANOVA.

Results:

Smoothing images improves brain extraction results using BET for all measures except specificity (all

p

b 0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35.

Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher

median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014,

p b 0.001), accuracy (0.9971 vs.

0.9971;median difference: 0.0001,

p b 0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p b 0.001)

and lower speci

ficity (0.9981 vs. 0.9982; median difference: −0.0001, p b 0.001). These measures are all very

high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using

smoothed data and an FI of 0.01, themean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates

the ICV estimates are similar for automated and manual segmentation.

In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929,

95% CI: 0.91, 0.945) for successfully extracted brains.