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= Cross-sectional and longitudinal segmentation for the following structures: thalamus, brainstem, hippo-amygdala =

'''''This functionality is available in [[https://surfer.nmr.mgh.harvard.edu/fswiki/ReleaseNotes|FreeSurfer 7]].'''''

If you use these tools in your analysis, please cite:

 * Thalamus: [[https://arxiv.org/abs/1806.08634|A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology]]. Iglesias, J.E., Insausti, R., Lerma-Usabiaga, G., Bocchetta, M., Van Leemput, K., Greve, D., van der Kouwe, A., Caballero-Gaudes, C., Paz-Alonso, P. Neuroimage (accepted).

 * Hippocampus: [[http://www.nmr.mgh.harvard.edu/~iglesias/pdf/subfieldsNeuroimage2015preprint.pdf|A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI]]. Iglesias, J.E., Augustinack, J.C., Nguyen, K., Player, C.M., Player, A., Wright, M., Roy, N., Frosch, M.P., Mc Kee, A.C., Wald, L.L., Fischl, B., and Van Leemput, K. Neuroimage, 115, July 2015, 117-137.

 * Amygdala: [[http://www.nmr.mgh.harvard.edu/~iglesias/pdf/AmygdalaAtlas.pdf|High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas]]. Saygin ZM & Kliemann D (joint 1st authors), Iglesias JE, van der Kouwe AJW, Boyd E, Reuter M, Stevens A, Van Leemput K, Mc Kee A, Frosch MP, Fischl B, Augustinack JC. Neuroimage, 155, July 2017, 370-382.

 * Brainstem: [[http://www.nmr.mgh.harvard.edu/~iglesias/pdf/Neuroimage_2015_brainstem.pdf|Bayesian segmentation of brainstem structures in MRI]]. Iglesias, J.E., Van Leemput, K., Bhatt, P., Casillas, C., Dutt, S., Schuff, N., Truran-Sacrey, D., Boxer, A., and Fischl, B. NeuroImage, 113, June 2015, 184-195.

 * Longitudinal method: [[http://www.nmr.mgh.harvard.edu/~iglesias/pdf/Neuroimage_2016_longitudinal.pdf|Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases]]. Iglesias JE, Van Leemput K, Augustinack J, Insausti R, Fischl B, Reuter M. Neuroimage, 141, November 2016, 542-555.

See also: [[HippocampalSubfields]], [[LongitudinalHippocampalSubfields]], [[HippocampalSubfieldsAndNucleiOfAmygdala]], [[BrainstemSubstructures]], [[ThalamicNuclei]]

<<BR>>

=== Contents ===
 1. Motivation and General Description
 2. Usage
 3. Positional Arguments

<<BR>>

=== 1. Motivation and General Description ===
This tool is compilation of our cross-sectional and longitudinal subregion segmentation tools for the thalamus, hippo-amygdala, and brainstem regions and ports their implementation from Matlab into python. This subregion segmentation tool accomplishes the following:

(a) This tool produces a parcellation of the thalamus into 25 different nuclei, using a probabilistic atlas built with histological data. The parcellation is based on structural MRI, either the main T1 scan processed through recon-all, or an additional scan of a different modality, which potentially shows better contrast between the nuclei.

(b) The tool uses a probabilistic atlas built with ultra-high resolution ex vivo MRI data (~0.1 mm isotropic) to produce an automated segmentation of the hippocampal substructures and the nuclei of the amygdala. The tool can use high-resolution images when available (typically, but not necessarily, T2 weighted).

(c) This tool generates an automated segmentation of four different brainstem structures from the input T1 scan: medulla oblongata, pons, midbrain and superior cerebellar peduncle (SCP). We use a Bayesian segmentation algorithm that relies on a probabilistic atlas of the brainstem (and neighboring brain structures) built upon manual delineations of the structures on interest in 49 scans (10 for the brainstem structures, 39 for the surrounding structures).


=== 2. Usage ===
This software requires that a whole brain T1 scan of the subject has been analyzed with the main !FreeSurfer stream ("recon-all"), i.e., we will assume that the command similar to this has already been run:

{{{
recon-all -all -s bert
}}}
where bert is the name of the subject. Bear in mind that, if this T1 scan has a voxel size smaller than 1 mm, it is possible to exploit this higher resolution by using the flag -cm in recon-all. Otherwise, both recon-all and this tool will work with a resampled version of the T1 volume at 1mm isotropic resolution. If we want to use the longitudinal version of this tool described in (Iglesias et al., Neuroimage, 2017), then it is also a requirement that the data have been processed with the longitudinal stream of !FreeSurfer (LongitudinalProcessing).

In !FreeSurfer 7, this module is called independently from recon-all. To analyze your subject "bert", you would simply type:

{{{
segment_subregions [STRUCTURE] [SUBJ]
}}}

The required arguments [STRUCTURE] has 3 possible options: thalamus, hippo-amygdala, and brainstem, while [SUBJ] would be your subject name, in this case $SUBJECT_DIR/bert.

To segment the thalamic nuclei for subject bert, for example, in a cross-sectional analysis:

{{{
segment_subregions thalamus --cross bert
}}}

Similarly, for a longitudinal analysis, run:

{{{
segment_subregions thalamus --long-base base --long-tps m00.long.base m24.long.base
}}}

where each argument to --long-tps represents a subject timepoint. Output segmentations and computed structure volumes will be saved to the subject's 'mri' subdirectory.


=== 3. Positional Arguments ===

== Required Arguments ==
||structure ||thalamus, hippo-amygdala, brainstem ||
== Optional Arguments ==
||-h, --help ||show this help message and exit ||
||--cross CROSS ||Subject to segment in cross-sectional analysis ||
||--long-tps LONG_TPS [LONG_TPS ...] ||Subject timepoints to segment in longitudinal analysis ||
||--long-base LONG_BASE ||Base subject for longitudinal analysis ||
||--sd SD ||Specify subjects directory (will override SUBJECTS_DIR env variable) ||
||--suffix SUFFIX ||Optional output file suffix ||
||--temp-dir TEMP_DIR ||Use alternative temporary directory. This will get deleted unless --debug is enabled ||
||--out-dir OUT_DIR ||Use alternative output directory (only for cross-sectional). Default is the subject's `mri` directory ||
||--debug ||Write intermediate debugging outputs ||
||--threads THREADS ||Number of threads to use. Defaults to 1 ||

Cross-sectional and longitudinal segmentation for the following structures: thalamus, brainstem, hippo-amygdala

This functionality is available in FreeSurfer 7.

If you use these tools in your analysis, please cite:

See also: HippocampalSubfields, LongitudinalHippocampalSubfields, HippocampalSubfieldsAndNucleiOfAmygdala, BrainstemSubstructures, ThalamicNuclei


Contents

  1. Motivation and General Description
  2. Usage
  3. Positional Arguments


1. Motivation and General Description

This tool is compilation of our cross-sectional and longitudinal subregion segmentation tools for the thalamus, hippo-amygdala, and brainstem regions and ports their implementation from Matlab into python. This subregion segmentation tool accomplishes the following:

(a) This tool produces a parcellation of the thalamus into 25 different nuclei, using a probabilistic atlas built with histological data. The parcellation is based on structural MRI, either the main T1 scan processed through recon-all, or an additional scan of a different modality, which potentially shows better contrast between the nuclei.

(b) The tool uses a probabilistic atlas built with ultra-high resolution ex vivo MRI data (~0.1 mm isotropic) to produce an automated segmentation of the hippocampal substructures and the nuclei of the amygdala. The tool can use high-resolution images when available (typically, but not necessarily, T2 weighted).

(c) This tool generates an automated segmentation of four different brainstem structures from the input T1 scan: medulla oblongata, pons, midbrain and superior cerebellar peduncle (SCP). We use a Bayesian segmentation algorithm that relies on a probabilistic atlas of the brainstem (and neighboring brain structures) built upon manual delineations of the structures on interest in 49 scans (10 for the brainstem structures, 39 for the surrounding structures).

2. Usage

This software requires that a whole brain T1 scan of the subject has been analyzed with the main FreeSurfer stream ("recon-all"), i.e., we will assume that the command similar to this has already been run:

recon-all -all -s bert

where bert is the name of the subject. Bear in mind that, if this T1 scan has a voxel size smaller than 1 mm, it is possible to exploit this higher resolution by using the flag -cm in recon-all. Otherwise, both recon-all and this tool will work with a resampled version of the T1 volume at 1mm isotropic resolution. If we want to use the longitudinal version of this tool described in (Iglesias et al., Neuroimage, 2017), then it is also a requirement that the data have been processed with the longitudinal stream of FreeSurfer (LongitudinalProcessing).

In FreeSurfer 7, this module is called independently from recon-all. To analyze your subject "bert", you would simply type:

segment_subregions [STRUCTURE]  [SUBJ]

The required arguments [STRUCTURE] has 3 possible options: thalamus, hippo-amygdala, and brainstem, while [SUBJ] would be your subject name, in this case $SUBJECT_DIR/bert.

To segment the thalamic nuclei for subject bert, for example, in a cross-sectional analysis:

segment_subregions thalamus --cross bert

Similarly, for a longitudinal analysis, run:

segment_subregions thalamus --long-base base --long-tps m00.long.base m24.long.base

where each argument to --long-tps represents a subject timepoint. Output segmentations and computed structure volumes will be saved to the subject's 'mri' subdirectory.

3. Positional Arguments

Required Arguments

structure

thalamus, hippo-amygdala, brainstem

Optional Arguments

-h, --help

show this help message and exit

--cross CROSS

Subject to segment in cross-sectional analysis

--long-tps LONG_TPS [LONG_TPS ...]

Subject timepoints to segment in longitudinal analysis

--long-base LONG_BASE

Base subject for longitudinal analysis

--sd SD

Specify subjects directory (will override SUBJECTS_DIR env variable)

--suffix SUFFIX

Optional output file suffix

--temp-dir TEMP_DIR

Use alternative temporary directory. This will get deleted unless --debug is enabled

--out-dir OUT_DIR

Use alternative output directory (only for cross-sectional). Default is the subject's mri directory

--debug

Write intermediate debugging outputs

--threads THREADS

Number of threads to use. Defaults to 1

SubregionSegmentation (last edited 2023-11-20 13:23:42 by JuanIglesias)