Brainlife App (wrapper) for dMRI based fiber tracking between ROIs using parallel transport tractography
Datasets and derivatives generated for Brad Caron's Qualifying exams.
A quality check application for tractography, segmentatations, and LiFE structures. For the whole brain tractogram, provides a number of statistics associated with average streamline characteristics (i.e. count, volume occupied, avg length, length distribution). Does the same for the positively weighted streamlines of an FE structure if input. If a classification structure is input, provides a number of macrostructural statistics like stream count, volume, avg length, whole brain count/volume proportion, etc).
Code of Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation.
This app will generate endpoint maps for all tracts in an input classification structure. User can specify what sort of decay/smoothing algorithm can be used (or none).
This is a brainlife.io wrapper app for mbaComputeFibersOutliers algorithm. It takes an existing tract classification and prune classified fibers that are unlike other fibers within the same tract.
This App takes tract masks and convert them to series of numerical values that chracaterizes each masks. The numerical values are generated from the flattened output of 3D convolutional layers of the model trained to classify tract names. Output values could be used as a "shape signature" and compared against other similar shaped tracts.
This app segments tracts using pairs of (potentially multiple) specified atlas regions to output a set of tracts. Instead of running this App, we recommend running "ROI Generation" App first to convert the Atlas to ROI, then run "WMA Tract Segmentation with ROIs" to segment tracts using those ROIs.
This app segments tracts using pairs of (potentially multiple) rois from an input directory to output a set of tracts.
This service creates 4 figures of each AFQ tract: axial, left sagittal, right sagittal, coronal. Please choose the t1 image slices you would like displayed. The default slices work well for the HCP t1 images if they have not been re-ACPC aligned. If you have ACPC aligned your t1 images using the ACPC alignment app on Brain Life the following values are a good starting point: coronal = 105, sagittal = 89, axial = 65....
This App creates a large set of candidate streamlines using an ensemble of algorithms and parameter values. All outputs will be then combined into a single track.tck output.
copy of ensemble tracking that runs from the master branch for testing new changes
To monitor app-stage
Classifies streamlines into known anatomical tracts.
repeat of fixed parameters for OHBM 2019 submission
White matter bundle segmentation as multiple Linear Assignment Problems (multi-LAP).
White matter bundle segmentation as multiple Linear Assignment Problems (multi-LAP). It also performs Nearest Neighbor (NN, or multi-NN) segmentation for comparison. WARNING: This App was used specifically for a Reproducibility Study and has been deprecated.
Compute the degree of overlap between two bundle masks using the Dice Similarity Coefficient (DSC) score.
Generates specified ROIS (in nifti format) from an input atlas.
This will fit the constrained spherical deconvolution model to an input DWI image. The outputs from this can be used for subsequent tracking apps.
This fits the NODDI model on single shell data. The only value that should be trusted is orientation dispersion (OD), as this measure can be fit accurately with single shell data