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<<Navigation(children)>>
'''Index'''
<<TableOfContents>>
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mri_ca_label - ? mri_ca_label

= Description =
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mri_ca_label [<options>] [input volume(s)] [xform] [gca file] [output volume] mri_ca_label [<options>] invol1 [invol2 ...] xform gcafile outvol
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= Positional Arguments =
|| [input volume(s)] || input volume(s) ||
|| [xform] || ? ||
|| [gca file] || ? ||
|| [output volume] || output volume ||		 = Arguments =
== Positional Arguments ==
|| invol1 [invol2 ...]  || input volume(s) ||
|| xform || transform file ||
|| gcafile || ? ||
|| outvol || output volume ||
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= Required Flagged Arguments =
none		 == Required Flagged Arguments ==
None
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= Optional Flagged Arguments = == Optional Flagged Arguments ==
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|| -wm [path] || use wm segmentation || || -wm path || use wm segmentation ||
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|| -t1 [gca_t1] || use file to label thin temporal lobe ||
|| -debug_voxel [x] [y] [z] || debug voxel ||
|| -debug_node [x] [y] [z] || debug node ||
|| -debug_label [int n] || debug label ||
|| -tr [float n] || set TR in msec ||
|| -te [float n] || set TE in msec ||
|| -alpha [float n] || set alpha in radians ||
|| -example [mri_vol] [segmentation] || use T1 (mri_vol) and segmentation as example ||
|| -pthresh [float n] || use p threshold n for adaptive renormalization (default=.7) ||
|| -niter [int n] || apply max likelihood for n iterations (default=2) ||		 || -t1 gca_t1 || use file to label thin temporal lobe ||
|| -debug_voxel x y z  || debug voxel ||
|| -debug_node x y z || debug node ||
|| -debug_label n || debug label ||
|| -tr TR || set TR in msec ||
|| -te TE || set TE in msec ||
|| -alpha ALPHA || set alpha in radians ||
|| -example mri_vol segmentation || use T1 (mri_vol) and segmentation as example ||
|| -pthresh thresh || use p threshold n for adaptive renormalization (default=.7) ||
|| -niter n || apply max likelihood for n iterations (default=2) ||
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|| -regularize [float n] || regularize variance to be sigma+nC(noise) || || -regularize reg || regularize variance to be sigma+nC(noise) ||
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|| -fwm [mri_vol] || use fixed white matter segmentation from wm ||
|| -mri [mri_vol] || write most likely MR volume to mri_vol ||
|| -heq [mri_vol] || use histogram equalization from mri_vol ||
|| -renorm [mri_vol] || renormalize using predicted intensity values in mri_vol ||		 || -fwm mri_vol || use fixed white matter segmentation from wm ||
|| -mri mri_vol || write most likely MR volume to mri_vol ||
|| -heq mri_vol || use histogram equalization from mri_vol ||
|| -renorm mri_vol || renormalize using predicted intensity values in mri_vol ||
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|| -flash_params [filename] || use FLASH forward model and tissue params in filename to predict ||
|| -renormalize [wsize] [iter] || reenorm class means [iter] times after initial label with window of [wsize] ||
|| -r [mri_vol] || set input volume ||		 || -flash_params filename || use FLASH forward model and tissue params in filename to predict ||
|| -renormalize wsize iter || reenorm class means [iter] times after initial label with window of [wsize] ||
|| -r mri_vol || set input volume ||
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|| -a [int n] || mean filter n time to conditional densities ||
|| -w [int n] [filename] || write snapshots of gibbs process every n times to filename ||
|| -m [mri_vol] || use mri_vol to mask final labeling ||
|| -e [int n] || expand ||
|| -n [int n] || set max iterations to n (default=200) ||
|| -f [int f] [float t] || filter labeled volume with threshold t (default=.5) mode filter f (default=0)times ||		 || -a n || mean filter n time to conditional densities ||
|| -w n filename || write snapshots of gibbs process every n times to filename ||
|| -m mri_vol || use mri_vol to mask final labeling ||
|| -e n || expand ||
|| -n n || set max iterations to n (default=200) ||
|| -f f thresh || filter labeled volume with threshold thresh (default=.5) mode filter f (default=0)times ||
|| -nowmsa || disables WMSA labels (hypo/hyper-intensities), selects second most probable label for each WMSA labelled voxel ||
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|| output volume || ? || || outvol || output volume ||
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= Description =
description
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command foo -i f -o out
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description

== Example 2 ==
command foo -i f -o out -f fvalue

description
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["othercommand1"], ["othercommand2"] [[mri_cc]]
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= Methods Description =
{{{
description
description
}}}

= References =
["References/Lastname###"]
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JaneSmith BruceFischl

Index

Contents

  1. Name
  2. Description
  3. Synopsis
  4. Arguments
    1. Positional Arguments
    2. Required Flagged Arguments
    3. Optional Flagged Arguments
  5. Outputs
  6. Examples
    1. Example 1
  7. Bugs
  8. See Also
  9. Links
  10. Reporting Bugs
  11. Author/s

Name

mri_ca_label

Description

Synopsis

mri_ca_label [<options>] invol1 [invol2 ...] xform gcafile outvol

Arguments

Positional Arguments

invol1 [invol2 ...]

input volume(s)

xform

transform file

gcafile

?

outvol

output volume

Required Flagged Arguments

None

Optional Flagged Arguments

-cross-sequence

label a volume acquired with sequence different than atlas

-nogibbs

disable gibbs priors

-wm path

use wm segmentation

-conform

interpolate volume to be isotropic 1mm^3

-t1 gca_t1

use file to label thin temporal lobe

-debug_voxel x y z

debug voxel

-debug_node x y z

debug node

-debug_label n

debug label

-tr TR

set TR in msec

-te TE

set TE in msec

-alpha ALPHA

set alpha in radians

-example mri_vol segmentation

use T1 (mri_vol) and segmentation as example

-pthresh thresh

use p threshold n for adaptive renormalization (default=.7)

-niter n

apply max likelihood for n iterations (default=2)

-novar

do not use variance in classification

-regularize reg

regularize variance to be sigma+nC(noise)

-nohippo

do not auto-edit hippocampus

-fwm mri_vol

use fixed white matter segmentation from wm

-mri mri_vol

write most likely MR volume to mri_vol

-heq mri_vol

use histogram equalization from mri_vol

-renorm mri_vol

renormalize using predicted intensity values in mri_vol

-flash

use FLASH forward model to predict intensity values

-flash_params filename

use FLASH forward model and tissue params in filename to predict

-renormalize wsize iter

reenorm class means [iter] times after initial label with window of [wsize]

-r mri_vol

set input volume

-h

use GCA to histogram normalize input image

-a n

mean filter n time to conditional densities

-w n filename

write snapshots of gibbs process every n times to filename

-m mri_vol

use mri_vol to mask final labeling

-e n

expand

-n n

set max iterations to n (default=200)

-f f thresh

filter labeled volume with threshold thresh (default=.5) mode filter f (default=0)times

-nowmsa

disables WMSA labels (hypo/hyper-intensities), selects second most probable label for each WMSA labelled voxel

Outputs

outvol

output volume

Examples

Example 1

Bugs

None

See Also

mri_cc

Links

FreeSurfer, FsFast

Reporting Bugs

Report bugs to <analysis-bugs@nmr.mgh.harvard.edu>

Author/s

BruceFischl

mri_ca_label (last edited 2017-12-08 11:54:44 by MorganFogarty)