Pipeline for Analysing Lesions after Stroke (PALS) is a scalable and user-friendly toolbox designed to facilitate standardised analysis and ensure quality in stroke research using T1-weighted MRIs

Software that functionally segments white matter connections to generate task-specific subcomponents of fiber bundles.

A collection of jupyter notebooks designed to provide a comprehensive exploration of concepts related to white matter segmentation.

Code of Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation.

Brain white matter hyperintensity segmentation, with T1 and FLAIR MRI images, using UNet.

A deep learning model that allows robust SC/GM multi-class segmentation based on ultra-high resolution 7T T2*-w MR images

Brainlife App for MIC-DKFZ/TractSeg. A tool for fast and accurate white matter bundle segmentation from Diffusion MRI using pretrained pytorch ML model.

Segmentation and analysis of vertical white matter tracts

Code to run Classifyber as a pre-trained bundle segmentation method.

White matter bundle segmentation as Anatomically-Informed multiple Linear Assignment Problems (multi-LAP-anat).

White matter bundle segmentation as multiple Linear Assignment Problems (multi-LAP).

Automatically segment a tractogram into categories (i.e. fronto-parietal tracts, parieto-temporal tracts, etc). THIS APPLICATION IS HIGHLY RECOMMENDED AS A MEANS OF RUNNING AN INITIAL QUALITY ASSURANCE CHECK ON YOUR GENERATED TRACTOGRAPHY OR AS A SANITY CHECK ON PROBLEMATIC SEGMENTATIONS.