= SynthSeg =
'''''This functionality is now available in FreeSurfer (v7.3.2 onwards)'''''
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'''''Update September 2022: SynthSeg is also available as part of Matlab (R2022b and onwards); see details below.'''''
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'''''Update December 2023: If you have images with white matter lesions and/or acquired at low-field (e.g., with a portable scanner), please try [[https://surfer.nmr.mgh.harvard.edu/fswiki/WMH-SynthSeg|WMH-SynthSeg]].'''''
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''Author: Benjamin Billot''
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''E-mail: benjamin.billot.18 [at] ucl.ac.uk''
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''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''
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If you use SynthSeg in your analysis, please cite:
* [[https://www.sciencedirect.com/science/article/pii/S1361841523000506|SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining]]. B Billot, DN Greve, O Puonti, A Thielscher, K Van Leemput, B Fischl, AV Dalca, JE Iglesias. Medical Image Analysis, 83, 102789 (2023).
For the robust mode (see below), please cite:
* [[https://www.pnas.org/doi/10.1073/pnas.2216399120|Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets]]. B Billot, C Magdamo, SE Arnold, S Das, JE Iglesias. PNAS, 120(9), e2216399120 (2023).
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=== Contents ===
1. General Description
2. Installation
3. Usage
4. Processing CT scans
5. Frequently asked questions (FAQ)
6. Matlab implementation
7. List of segmented structures
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=== 1. General Description ===
This tool implements SynthSeg, the first convolutional neural network for segmentation of brain MRI scans of any contrast and resolution that works out-of-the-box, without retraining or fine-tuning.
SynthSeg relies on a single model, which we distribute here. This model is robust to:
* a wide array of subject populations: from young and healthy to ageing and diseased subjects with strong atrophy,
* white matter lesions (see green arrows in image below),
* scans with or without preprocessing (bias field corruption, skull stripping, intensity normalisation, registration to template).
The output segmentations are returned at high resolution (1mm isotropic), regardless of the resolution of the input scans.
The code can run on the GPU (6s per scan) as well as the CPU (2 minutes per scan).
The list of segmented structures can be found at the bottom of this page.
< > {{attachment:segm2.png||height="320"}} < >< >
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=== New features (28/06/2022) ===
SynthSeg 1.0 has now been replaced with SynthSeg 2.0, which offers more functionalities !! These new features are illustrated in the figure below.
Specifically, in addition to whole-brain segmentation, SynthSeg is now also able to perform cortical parcellation, automated quality
control (QC) of the produced segmentations, and intracranial volume (ICV) estimation computed by including the CSF to the list of segmented structures.
< > {{attachment:new_features2.png||height="320"}}
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=== Robust version ===
While SynthSeg is quite robust, it sometimes falters on scans with low signal-to-noise ratio, or with low tissue contrast (see figure below).
For this reason, we developed a new architecture for increased robustness, named "SynthSeg-robust", which can be selected with the --robust flag (see Section 3).
You can use this mode when SynthSeg gives results like those in the third column (1st and 2nd rows) of the figure below:
< > {{attachment:robust2.png||height="320"}}
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See the table below for a summary of the functionalities supported by each version.
< > {{attachment:table_versions2.png||height="180"}}
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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.
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.
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=== 3. Usage ===
You can use SynthSeg with the following command:
{{{
mri_synthseg --i --o