Verifyber

A supervised tractogram filtering method based on Geometric Deep Learning. It allows the filtering out of non-plausible streamlines from tractograms

Tractogram filtering script

The executable script is tractogram_filtering.py. It reads the configuration file, run_config.json to get arguments from "outside", and based on it performs different steps.

The script generates a temporary folder TEMP=tmp_tractogram_filtering/, where it stores in the subdirectories TEMP/input/ and TEMP/output/ the actual input and output files. Some intermediate files generated during the pre-processing steps are stored directly in the TEMP folder

The input file is always a tractogram .trk, projected into MNI space with fixed number of points per streamline.

The output are two text files containing the indexes of plausible and non-plausible fibers, and optionally the .trk of the filtered tractogram.

Configuration file

run_config.json is composed as follows:

• trk: path to the tractogram uploaded by the user
• t1: path to the T1w image in subject space. The image is preferred if it is a brain extracted image. In case no t1 or fa image is provided, the tractogram is assumed to be already in MNI space.
• fa: path to the FA image in subject space. The image is preferred if it is a brain extracted image. In case no t1 or fa image is provided, the tractogram is assumed to be already in MNI space.
• resample_points: T/F flag. If T the streamlines will be resampled to 16 points, otherwise no.
• return_trk: T/F flag. If T the filtered trk tractogram will be returned along with the indexes of plausible and non-plausible streamlines.
• task: classification/regression. [not used right now]
• warp: choices (lin | fast | slow). Type of co-registration to the standard using ANTs normalization tool. "lin" is a affine registration; "fast" (SUGGESTED) is a quick non-linear diffeormophic registration; "slow" is a more accurate non-linear diffeormophic registratio, requiring more time to compute.
• model: defalut = "sdec_extractor", choices are the names of the folder present in checkpoints/

Usage

1. setup your env follwing instructions in verifyber_updated_env.txt
2. run tractogram_filtering.py -config <run_config.json>

Docker containers

See docker://pietroastolfi/tractogram-filtering:, =cpu|gpu. Note that the gpu container works with CUDA 10