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= SuperSynth: Multi-task 3D U-net for scans of any resolution and contrast (including ex vivo) = <<BR>> ''Author: Juan Eugenio Iglesias'' <<BR>> ''E-mail: jiglesiasgonzalez [at] mgh.harvard.edu'' <<BR>> <<BR>> ''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'' <<BR>> <<BR>> Relevant publications: <<BR>> "A Modality-agnostic Multi-task Foundation Model for Human Brain Imaging" Liu et al., in preparation. <<BR>> === Contents === 1. General Description 2. Installation 3. Usage <<BR>> === 1. General Description === This is a U-Net trained to make a set of useful predictions from any 3D brain image (in vivo, ex vivo, single hemispheres, etc) using a common backbone. Like our other "Synth" tools (SynthSeg, SynthSR, SynthMorph...), it is trained on synthetic data to support inputs of any resolution and contrast It predicts: * Segmentation: * Registration to MNI atlas * Joint super-resolution and synthesis of 1mm isotropic T1w, T2w, and FLAIR scans. === 2. Installation === The first time you run this module, it will prompt you to download a machine learning model files. Follow the instructions on the screen to obtain the file. <<BR>> === 3. Usage === The entry point / main script is mri_super_synth. There are two way of running the code: a. For a single scan: just provide input file with --i, output directory with --o, and type of volume with --mode. b. For a set of scans: you need to prepare a CSV file, where each row has 3 columns separated with commas: * Column 1: input file * Column 2: output directory * Column 3: mode (must be invivo, exvivo, cerebrum, left-hemi, or right-hemi) * Please note that there is no leading/header row in the CSV file. The first row already corresponds to an input volume. Tip: you can comment out a line by starting it with # Important note: for 32 vs 64-bit reasons, inference is tiled on the GPU but not on the CPU, so results are expected to be slightly different on the 2 platforms. You can use --force_tiling option on the CPU to force tiling and get the same results as on the GPU The command line options are: * --i [IMAGE_OR_CSV_FILE] Input image to segment - mode A - or CSV file with list of scans - mode B (required argument) * --o [OUTPUT_DIRECTORY] Directory where outputs will be written (ignored in mode B) * --mode [MODE] Type of input. Must be invivo, exvivo, cerebrum, left-hemi, or right-hemi (ignored in mode B) * --threads [THREADS] Number of cores to be used. You can use -1 to use all available cores. Default is -1 (optional) * --device [DEV] Device used for computations (cpu or cuda). The default is to use cuda if a GPU is available (optional) * --force_tiling Use this flag to force tiling on CPU and get the same results as on GPU, as explained above (optional) |
SuperSynth: Multi-task 3D U-net for scans of any resolution and contrast (including ex vivo)
Author: Juan Eugenio Iglesias
E-mail: jiglesiasgonzalez [at] 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
Relevant publications:
"A Modality-agnostic Multi-task Foundation Model for Human Brain Imaging" Liu et al., in preparation.
Contents
- General Description
- Installation
- Usage
1. General Description
This is a U-Net trained to make a set of useful predictions from any 3D brain image (in vivo, ex vivo, single hemispheres, etc) using a common backbone. Like our other "Synth" tools (SynthSeg, SynthSR, SynthMorph...), it is trained on synthetic data to support inputs of any resolution and contrast It predicts:
- Segmentation:
- Registration to MNI atlas
- Joint super-resolution and synthesis of 1mm isotropic T1w, T2w, and FLAIR scans.
2. Installation
The first time you run this module, it will prompt you to download a machine learning model files. Follow the instructions on the screen to obtain the file.
3. Usage
The entry point / main script is mri_super_synth. There are two way of running the code:
- For a single scan: just provide input file with --i, output directory with --o, and type of volume with --mode. b. For a set of scans: you need to prepare a CSV file, where each row has 3 columns separated with commas:
* Column 1: input file * Column 2: output directory * Column 3: mode (must be invivo, exvivo, cerebrum, left-hemi, or right-hemi) * Please note that there is no leading/header row in the CSV file. The first row already corresponds to an input volume. Tip: you can comment out a line by starting it with #
Important note: for 32 vs 64-bit reasons, inference is tiled on the GPU but not on the CPU, so results are expected to be slightly different on the 2 platforms. You can use --force_tiling option on the CPU to force tiling and get the same results as on the GPU
The command line options are:
- --i [IMAGE_OR_CSV_FILE] Input image to segment - mode A - or CSV file with list of scans - mode B (required argument)
--o [OUTPUT_DIRECTORY] Directory where outputs will be written (ignored in mode
--mode [MODE] Type of input. Must be invivo, exvivo, cerebrum, left-hemi, or right-hemi (ignored in mode
- --threads [THREADS] Number of cores to be used. You can use -1 to use all available cores. Default is -1 (optional)
- --device [DEV]
Device used for computations (cpu or cuda). The default is to use cuda if a GPU is available (optional)
- --force_tiling
Use this flag to force tiling on CPU and get the same results as on GPU, as explained above (optional)