Re: [Freesurfer] freesufer v6 beta - T2 pial refinement performance and nsigma_below setting

2016-11-30 Thread Matt Glasser
Hi Antonin,

In our experience with FreeSurfer 5.3 we were getting very consistent
results.  We are going to try some of this locally and work with the
FreeSurfer folks to get the T2w stream working robustly in FreeSurfer v6.0+.

Thanks for testing this,

Matt.

From:  <freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of Antonin Skoch
<a...@ikem.cz>
Reply-To:  Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Date:  Wednesday, November 30, 2016 at 3:31 PM
To:  <freesurfer@nmr.mgh.harvard.edu>
Subject:  Re: [Freesurfer] freesufer v6 beta - T2 pial refinement
performance and nsigma_below setting

Dear experts,

given sub-optimal results of my surfaces I referred to in my previous post,
I have tested several different settings and input data preparation to
obtain optimal pial surfaces in my modified HCP pipeline. I tested all
variants in 5 representative subjects.

My modified HCP pipeline uses FreeSurfer V6beta and full hires recon (-cm
option) with 0.7mm3 data (including FreeSurferHiResWhite.sh and
FreeSurferHiResPial.sh where effectively no up and downsampling is done
since all steps are done in original 0.7 mm3 resolution).

I have played with input data and parameters of 2nd pass mris_make_surfaces
-T2pial in FreeSurferHiResPial.sh
Since FreeSurferHiResPial.sh does not use aseg-informed and white
surface-informed normalization and brain masking of T2 (which is default in
recon-all), I also tested performance of this option (using commands
borrowed from recon-all -T2pial). Since -nsigma_below 3 (default in
FreeSurferHiResPial.sh) with combination of freeSurfer V6beta version of
mris_make_surfaces cuts out some portions of gray matter in my data (in
contrast to FreeSurfer 5.3 version where it seems that nsigma_below 3
suffices!), I increased -nsigma_below to 5 (which is default value used in
recon-all -T2pial).

My tested versions were following:
For modified full hires HCP pipeline with FreeSurfer V6beta:
1. -nsigma_below 3 (original FreeSurferHiResPial.sh)
2. -nsigma_below 5 
3. -nsigma_below 5 with additional masking of T2 by brainmask.mgz
4. -nsigma_below 5 with aseg-informed normalization of T2 (using
mri_normalize)
5. -nsigma_below 5 with aseg-informed normalization of T2 (using
mri_normalize) and masking of T2 by brainmask.mgz

And, as Matt suggested, I also tried:
6. standard FreeSurfer V6 beta recon-all -pial and -T2pial (apart from
standard recon-all I used as ?h.white input the output of
FreeSurferHiResWhite.sh , but I think it should not matter in this context).

My observations are following:

It is definitely needed to increase -nsigma_below from 3 due to significant
cut out of portions of gray matter in some regions, in some subjects these
regions are very large.

Increase nsigma_below to 5 prevents cutting out of gray matter, however it
leads to leak of surfaces to cerebellum (as I showed in my previous post)
for all my versions (to varying extent). I am not sure how much significant
the extend of error is, looking subjective it seems significant, but
accounting the fact that the data is of high resolution, the maximal error
would be approx. 2 mm.

-nsigma_below 5 with aseg-informed normalization does not work well in some
areas and results in cutting out gray matter in some regions (but not to
such great extend as in nsigma_below 3)

FreeSurfer V6beta tends in some (not very large) areas to cut out gray
matter. On the other hand, in some areas and in some subjects it produces
largest leak of surfaces to dura and cerebellum from all my tested versions.

mri_normalize of T2 has in some regions  sub-optimal performance on pial
surface estimation and actually performs worse than global (no aseg-informed
and no white surface-informed) normalization ( especially at temporal poles,
but also in some areas at convexity).

I have indecisive results with masking. In some subjects it had almost no
effect, in one subject it definitely improved the leak to dura, but in some
subjects the results were quite opposite. I am not sure why the masking
should matter since the mask is larger than the regions where the surfaces
are extending, but I am not very familiar with internals of
mris_make_surfaces (the masked T2 maybe after internal smoothing in
mris_make_surfaces can propagate the voxel values even to regions relevant
for surface estimation). Therefore I would tend to use the masking.

Overall best results (better than with V6beta recon-all) I obtained with
-nsigma_below 5 with global (no aseg-informed and white surface-informed)
normalization of T2 as it is done in FreeSurferHiResPial.sh.

My overall impresion: I was quite frustrated to observe, how much the
results are dependent on particular setting of the parameters and input
data. But maybe my expectations of accuracy of surfaces were unrealistically
high

Regards,

Antonin
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Re: [Freesurfer] freesufer v6 beta - T2 pial refinement performance and nsigma_below setting

2016-11-30 Thread Antonin Skoch
Dear experts,

given sub-optimal results of my surfaces I referred to in my previous post, I 
have tested several different settings and input data preparation to obtain 
optimal pial surfaces in my modified HCP pipeline. I tested all variants in 5 
representative subjects.

My modified HCP pipeline uses FreeSurfer V6beta and full hires recon (-cm 
option) with 0.7mm3 data (including FreeSurferHiResWhite.sh and 
FreeSurferHiResPial.sh where effectively no up and downsampling is done since 
all steps are done in original 0.7 mm3 resolution).

I have played with input data and parameters of 2nd pass mris_make_surfaces 
-T2pial in FreeSurferHiResPial.sh
Since FreeSurferHiResPial.sh does not use aseg-informed and white 
surface-informed normalization and brain masking of T2 (which is default in 
recon-all), I also tested performance of this option (using commands borrowed 
from recon-all -T2pial). Since -nsigma_below 3 (default in 
FreeSurferHiResPial.sh) with combination of freeSurfer V6beta version of 
mris_make_surfaces cuts out some portions of gray matter in my data (in 
contrast to FreeSurfer 5.3 version where it seems that nsigma_below 3 
suffices!), I increased -nsigma_below to 5 (which is default value used in 
recon-all -T2pial).

My tested versions were following:
For modified full hires HCP pipeline with FreeSurfer V6beta:
1. -nsigma_below 3 (original FreeSurferHiResPial.sh)
2. -nsigma_below 5 
3. -nsigma_below 5 with additional masking of T2 by brainmask.mgz
4. -nsigma_below 5 with aseg-informed normalization of T2 (using mri_normalize)
5. -nsigma_below 5 with aseg-informed normalization of T2 (using mri_normalize) 
and masking of T2 by brainmask.mgz

And, as Matt suggested, I also tried:
6. standard FreeSurfer V6 beta recon-all -pial and -T2pial (apart from standard 
recon-all I used as ?h.white input the output of FreeSurferHiResWhite.sh , but 
I think it should not matter in this context).

My observations are following:

It is definitely needed to increase -nsigma_below from 3 due to significant cut 
out of portions of gray matter in some regions, in some subjects these regions 
are very large.

Increase nsigma_below to 5 prevents cutting out of gray matter, however it 
leads to leak of surfaces to cerebellum (as I showed in my previous post) for 
all my versions (to varying extent). I am not sure how much significant the 
extend of error is, looking subjective it seems significant, but accounting the 
fact that the data is of high resolution, the maximal error would be approx. 2 
mm.

-nsigma_below 5 with aseg-informed normalization does not work well in some 
areas and results in cutting out gray matter in some regions (but not to such 
great extend as in nsigma_below 3)

FreeSurfer V6beta tends in some (not very large) areas to cut out gray matter. 
On the other hand, in some areas and in some subjects it produces largest leak 
of surfaces to dura and cerebellum from all my tested versions. 

mri_normalize of T2 has in some regionsĀ  sub-optimal performance on pial 
surface estimation and actually performs worse than global (no aseg-informed 
and no white surface-informed) normalization ( especially at temporal poles, 
but also in some areas at convexity). 

I have indecisive results with masking. In some subjects it had almost no 
effect, in one subject it definitely improved the leak to dura, but in some 
subjects the results were quite opposite. I am not sure why the masking should 
matter since the mask is larger than the regions where the surfaces are 
extending, but I am not very familiar with internals of mris_make_surfaces (the 
masked T2 maybe after internal smoothing in mris_make_surfaces can propagate 
the voxel values even to regions relevant for surface estimation). Therefore I 
would tend to use the masking.

Overall best results (better than with V6beta recon-all) I obtained with 
-nsigma_below 5 with global (no aseg-informed and white surface-informed) 
normalization of T2 as it is done in FreeSurferHiResPial.sh.

My overall impresion: I was quite frustrated to observe, how much the results 
are dependent on particular setting of the parameters and input data. But maybe 
my expectations of accuracy of surfaces were unrealistically high

Regards,

Antonin___
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Re: [Freesurfer] freesufer v6 beta - T2 pial refinement performance and nsigma_below setting

2016-11-27 Thread Matt Glasser
How does it work if you just use vanilla recon-all v6 beta with the T2
refinement flag and the hires option?  We havenĀ¹t yet updated the HCP
scripts for FreeSurfer v6, but this is on our to do list.

Peace,

Matt.

From:  <freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of Antonin Skoch
<a...@ikem.cz>
Reply-To:  Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Date:  Sunday, November 27, 2016 at 7:03 PM
To:  <freesurfer@nmr.mgh.harvard.edu>
Subject:  [Freesurfer] freesufer v6 beta - T2 pial refinement performance
and nsigma_below setting

Dear FreeSurfer experts,

I have processed a group of subjects by the freesurfer v6 beta version
(with recent upgrade of mris_make_surfaces from 2016/11/21).

It seems to me that in contrast to v5.3 version (with the same nsigma_above
2, nsigma_below 3) the T2 pial refinement cuts out too much gray matter in
regions where the gray matter has rather low intensity in T2 images. I have
observed this behavior quite often in my subjects.

I have uploaded the example subject cut_gm_T2pial.tar.gz to the server. The
problematic area has RAS location 3.9, -70, 0. Also see the screenshot.

My command line was:

mris_make_surfaces -nsigma_above 2 -nsigma_below 3 -aseg aseg.hires -filled
filled.hires -wm wm.hires -mgz -sdir $my_subject_dir -orig white.deformed
-nowhite -orig_white white.deformed -orig_pial pial.preT2.pass2 -T2
my_subject/mri/T2w_hires.norm -T1 T1w_hires.norm -output .postT2.pass2
my_subject rh

and for FS 5.3

mris_make_surfaces -nsigma_above 2 -nsigma_below 3 -aseg aseg.hires -filled
filled.hires -wm wm.hires -mgz -sdir $my_subject_dir -orig white.deformed
-nowhite -orig_white white.deformed -orig_pial pial.preT2.pass2 -T2
my_subject/mri/T2w_hires.norm -T1 T1w_hires.norm -output
.postT2.pass2.FS_5.3 my_subject rh

Could you please comment on how to proceed?

It seems that in contrast to v5.3 it is necessary to increase nsigma_below
value since the nsigma values seem to be more "sensitive" in v6beta version.
I tried to increase nsigma_below (nsigma_below for T2 in recon-all is 5) but
it seemed to me that it leads to "leak" of the pial surface out of the gray
matter in other areas, mainly in regions adjacent to cerebellum. See the
example leaking the pial surface to the cerebellum in left hemisphere with
nsigma_below 5 in the second screenshot in attachment ( I have uploaded this
subject as file cut_gm_T2pial2.tar.gz, his cut-out gray matter RAS location
is -33.7, -18, 45, command line is similar as above, but for left
hemisphere). Yellow surface is with nsigma_below 5, blue is nsigma_below 3.
Btw, the -aseg option in mris_make_surfaces does not prevent to leak
surfaces into the cerebellum?

Could also be the reason of these problems, that I used HCP scripts which do
not do the T2 normalization with anatomy-informed mri_normalize (using aseg
and white surface), but only global normalization and bias field correction?

Regards,

Antonin Skoch
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The information in this e-mail is intended only for the person to whom it is
addressed. If you believe this e-mail was sent to you in error and the e-mail
contains patient information, please contact the Partners Compliance HelpLine at
http://www.partners.org/complianceline . If the e-mail was sent to you in error
but does not contain patient information, please contact the sender and properly
dispose of the e-mail.