Name

mris_ca_train - Creates a cortical parcellation atlas file based on one or more annotated subjects. mris_ca_train builds probabilistic information estimated from a manually labeled training set (of annotated subjects). This information is then used by mris_ca_label to automatically assign a neuroanatomical label to each location on a cortical surface model. This procedure incorporates both geometric information derived from the cortical model (sulcus and curvature), and neuroanatomical convention, as found in the training set. The result of mris_ca_train and mris_ca_label is a complete labeling of cortical sulci and gyri.

Synopsis

mris_ca_train [options] <hemi> <canonsurf> <annotfile> <subject1> [subject2 ...] <outputfile>

Arguments

Positional Arguments

[options]	-sdir, -nbrs, -orig, -norm1, -norm2, -norm3, -ic, -sulc, -sulconly, -a, -t, -v, -n, -?, -u
<hemi>	hemisphere: rh or lh
<canonsurf>	canonical surface file
<annotfile>	annotation file
<subject1> [subject 2...]	subject(s)
<outputfile>	classifier array output file

Required Flagged Arguments

hemi canonsurf annotfile subject1 outputfile

Optional Flagged Arguments

-sdir <subject dir>	specify a subjects directory (default=$SUBJECTS_DIR)
-nbrs <number>	neighborhood size (default=2)
-orig <filename>	specify filename of original surface (default=smoothwm)
-norm1	GCSA normalize input #1 after reading (default: disabled)
-norm2	GCSA normalize input #2 after reading (default: disabled)
-norm3	GCSA normalize input #3 after reading (default: disabled)
-ic <number_priors> <number_classifiers>	parameters passed to GCSAalloc() routine (default: -ic 7 4)
-sulc	specify sulc as only input (default: sulcus and curvature)
-sulconly	same as -sulc
-a <number>	number of averages (default=5)
-t <filename>	specify parcellation table input file (default: none)
-v <number>	diagnostic level (default=0)
-n <number>	number of inputs (default=1)
-?	print usage info
-u	same as -?

Outputs

classifier array output file, containing probabilistic information estimated from the manually labeled training set

Example

mris_ca_train -n 2 \
    -t ./my_color_file.txt \
    lh \
    sphere.reg \
    my_manual_labeling \
    $SUBJECTS \
    ./lh.my_atlas.gcs

In this example, mris_ca_train would look for a file named lh.my_manual_labeling.annot in each of the subjects listed in $SUBJECTS label dir (e.g. $SUBJECTS_DIR/$s/label), and also assume that a file named lh.sphere.reg existed in the surf dir of each subject.

The -n 2 option tells it to use 2 feature dimensions for classification: curv and sulc (which is what is used by default).

The -t ./my_color_file.txt option will read in the file my_color_file.txt and embed it in the atlas, so that mris_ca_label will put it in the automatically generated .annot files, so that later, tksurfer (and other things) can read it in.

The format of the my_color_file.txt file consists of a set of lines like:

1   Corpus_callosum     50      50      50      0

where the last value (0, in this example) is not used, and the 50s are r,g,b (red,green,blue) values. They must match what is in the annot file, in which each vertex is given the value: r+(g << 8)+(b << 16).

Bugs

None

Links

CorticalParcellation, FreeSurfer, FsFast

References

[https://surfer.nmr.mgh.harvard.edu/ftp/articles/fischl04-parcellation.pdf Automatically Parcellating the Human Cerebral Cortex], Fischl et al., (2004). Cerebral Cortex, 14:11-22.

Reporting Bugs

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

Author/s

BruceFischl

-  ⇤ ← Revision 3 as of 2005-11-15 19:24:03 → 
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+   ← Revision 13 as of 2005-11-16 16:57:37 → ⇥
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  Editor: NickSchmansky
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
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-## Note: This page was created with the CommandTemplate
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## If you're modifying this page please take a look at the
## latest version of CommandTemplate to ensure that you're
## using the latest version of the CommandTemplate
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-mris_ca_train - ??
+'''mris_ca_train''' - Creates a cortical parcellation atlas file based on one or more annotated subjects.  mris_ca_train builds probabilistic information estimated from a manually labeled training set (of annotated subjects).  This information is then used by mris_ca_label to automatically assign a neuroanatomical label to each location on a cortical surface model.  This procedure incorporates both geometric information derived from the cortical model (sulcus and curvature), and neuroanatomical convention, as found in the training set. The result of mris_ca_train and mris_ca_label is a complete labeling of cortical sulci and gyri.
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-mris_ca_train [options] hemi canonsurf annotfile subject1 [subject2 ...] [outputfile]
+mris_ca_train [options] <hemi> <canonsurf> <annotfile> <subject1> [subject2 ...] <outputfile>
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+Line 14:
-|| hemi || hemisphere  ||   ||
|| canon surf || canonical surface ||   ||
|| annot file || annotation file || ||
|| subject 1 [subject 2...] ||  subject(s)||  ||
|| outputfile || output file ||
+|| [options] || -sdir, -nbrs, -orig, -norm1, -norm2, -norm3, -ic, -sulc, -sulconly, -a, -t, -v, -n, -?, -u ||
|| <hemi> || hemisphere: rh or lh ||
|| <canonsurf> || canonical surface file ||
|| <annotfile> || annotation file ||
|| <subject1> [subject 2...] || subject(s) ||
|| <outputfile> || classifier array output file ||
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+Line 22:
-none
+hemi canonsurf annotfile subject1 outputfile
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-none
+|| -sdir <subject dir> || specify a subjects directory (default=$SUBJECTS_DIR) ||
|| -nbrs <number> || neighborhood size (default=2) ||
|| -orig <filename> || specify filename of original surface (default=smoothwm) ||
|| -norm1 || GCSA normalize input #1 after reading (default: disabled) ||
|| -norm2 || GCSA normalize input #2 after reading (default: disabled) ||
|| -norm3 || GCSA normalize input #3 after reading (default: disabled) ||
|| -ic <number_priors> <number_classifiers> || parameters passed to GCSAalloc() routine (default: -ic 7 4) ||
|| -sulc || specify sulc as only input (default: sulcus and curvature) ||
|| -sulconly || same as -sulc ||
|| -a <number> || number of averages (default=5) ||
|| -t <filename> || specify parcellation table input file (default: none) ||
|| -v <number> || diagnostic level (default=0) ||
|| -n <number> || number of inputs (default=1) ||
|| -? || print usage info ||
|| -u || same as -? ||
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-||outpufile|| ||
+|| <outputfile> || classifier array output file, containing probabilistic information estimated from the manually labeled training set ||
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+Line 44:
-= Description =
??
+= Example =
{{{
mris_ca_train -n 2 \
    -t ./my_color_file.txt \
    lh \
    sphere.reg \
    my_manual_labeling \
    $SUBJECTS \
    ./lh.my_atlas.gcs
}}}
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+Line 55:
-= Examples =
== Example 1 ==
??
== Example 2 ==
??
+In this example, mris_ca_train would look for a file named '''lh.my_manual_labeling.annot''' in each of the subjects listed in $SUBJECTS label dir (e.g. $SUBJECTS_DIR/$s/label), and also assume that a file named '''lh.sphere.reg''' existed in the surf dir of each subject.

The '''-n 2''' option tells it to use 2 feature dimensions for classification: curv and sulc (which is what is used by default).

The '''-t ./my_color_file.txt''' option will read in the file '''my_color_file.txt''' and embed it in the atlas, so that mris_ca_label will put it in the automatically generated .annot files, so that later, tksurfer (and other things) can read it in.

The format of the '''my_color_file.txt''' file consists of a set of lines like:
{{{
1   Corpus_callosum     50      50      50      0
}}}
where the last value (0, in this example) is not used, and the 50s are r,g,b (red,green,blue) values. They must match what is in the annot file, in which each vertex is given the value: r+(g << 8)+(b << 16).
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+Line 71:
+["mris_ca_label"]
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+Line 74:
-FreeSurfer, FsFast

= Methods Description =
+CorticalParcellation, FreeSurfer, FsFast
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+[https://surfer.nmr.mgh.harvard.edu/ftp/articles/fischl04-parcellation.pdf Automatically Parcellating the Human Cerebral Cortex], Fischl et al., (2004). Cerebral Cortex, 14:11-22.