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=== 2. Installation === The first time you run this module, it will prompt you to install Tensorflow. Simply follow the instructions in the screen to install the CPU or GPU version. |
=== 2. Basic SAMSEG (cross-sectional processing) === |
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If you have a compatible GPU, you can install the GPU version for faster processing, but this requires installing libraries (GPU driver, Cuda, CuDNN). These libraries are generally required for a GPU, and are not specific for this tool. In fact you may have already installed them. In this case you can directly use this tool without taking any further actions, as the code will automatically run on your GPU. |
In its most basic form SAMSEG takes one or more co-registered MRI volumes as input, and produces an output segmentation in around 10 min on a good desktop computer (with multi-threading enabled). Preprocessing of the scan(s) with FreeSurfer is neither required nor recommended (e.g., no reformatting to 1mm isotropic resolution, no bias field correction and no skull stripping is needed nor recommended). The command line is: {{{ run_samseg --input <file> [<file> ...] --output <dir> [--threads <threads>] [--pallidum-separate] }}} where: * ''<file>'': is the path to the input volume(s) in NIFTI or MGZ file format. If you have more than one contrast (e.g., both T1w and T2w) you can simply list all the input contrasts that you want to use -- the only requirement is that all input volumes are co-registered with each other, and have the same image grid size and voxel dimensions. |
This page is readable only by those in the LcnGroup and CmetGroup.
Samseg (cross-sectional, longitudinal, MS lesions)
This functionality is available in FreeSurfer 7, with gradual improvements appearing in the development version.
Author: Koen Van Leemput
E-mail: koen [at] nmr.mgh.harvard.edu
Rather than directly contacting the author, please post your questions on this module to the FreeSurfer mailing list at freesurfer [at] nmr.mgh.harvard.edu
If you use these tools in your analysis, please cite:
Cross-sectional: Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling. O. Puonti, J.E. Iglesias, K. Van Leemput. Neuroimage, 143, 235-249, 2016.
Longitudinal: A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis. S. Cerri, A. Hoopes, D.N. Greve, M. Mühlau, K. Van Leemput. International Workshop on Machine Learning in Neuroimaging, 2020.
MS lesions: A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis. S. Cerri, O. Puonti, D.S. Meier, J. Wuerfel, M. Mühlau, H.R. Siebner, K. Van Leemput. 2020.
See also: ThalamicNuclei, HippocampalSubfieldsAndNucleiOfAmygdala, BrainstemSubstructures
1. General Description
Sequence Adaptive Multimodal SEGmentation (SAMSEG) is a tool to robustly segment dozens of brain structures from head MRI scans without preprocessing. The characteristic property of SAMSEG is that it accepts multi-contrast MRI data without prior assumptions on the specific type of scanner or pulse sequences used. Dedicated versions to handle longitudinal data, or to segment white matter lesions in multiple sclerosis (MS) patients are also available.
The figure below illustrates a typical SAMSEG segmentation result on a T1w-FLAIR scan of a MS patient:
2. Basic SAMSEG (cross-sectional processing)
In its most basic form SAMSEG takes one or more co-registered MRI volumes as input, and produces an output segmentation in around 10 min on a good desktop computer (with multi-threading enabled). Preprocessing of the scan(s) with FreeSurfer is neither required nor recommended (e.g., no reformatting to 1mm isotropic resolution, no bias field correction and no skull stripping is needed nor recommended). The command line is:
run_samseg --input <file> [<file> ...] --output <dir> [--threads <threads>] [--pallidum-separate]
where: * <file>: is the path to the input volume(s) in NIFTI or MGZ file format. If you have more than one contrast (e.g., both T1w and T2w) you can simply list all the input contrasts that you want to use -- the only requirement is that all input volumes are co-registered with each other, and have the same image grid size and voxel dimensions.