FastSurfer | PyTorch implementation of FastSurferCNN

FastSurfer is a Jupyter Notebook library. FastSurfer has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

This directory contains all information needed to run FastSurfer - a fast and accurate deep-learning based neuroimaging pipeline. This approach provides a full FreeSurfer alternative for volumetric analysis (within 1 minute) and surface-based thickness analysis (within only around 1h run time). The whole pipeline consists of two main parts:. (i) FastSurferCNN - an advanced deep learning architecture capable of whole brain segmentation into 95 classes in under 1 minute, mimicking FreeSurfer’s anatomical segmentation and cortical parcellation (DKTatlas). (ii) recon-surf - full FreeSurfer alternative for cortical surface reconstruction, mapping of cortical labels and traditional point-wise and ROI thickness analysis in approximately 60 minutes. Image input requirements are identical to FreeSurfer: good quality T1-weighted MRI acquired at 3T with a resolution close to 1mm isotropic (slice thickness should not exceed 1.5mm). Preferred sequence is Siemens MPRAGE or multi-echo MPRAGE. GE SPGR should also work. Sub-mm scans (e.g. .75 or .8mm isotropic) will be downsampled by us automatically to 1mm isotropic, for example, we had success segmenting de-faced HCP data. Within this repository, we provide the code and Docker files for running FastSurferCNN (segmentation only) and recon-surf (surface pipeline only) independently from each other or as a whole pipeline (run_fastsurfer.sh, segmentation + surface pipeline). For each of these purposes, see the README.md's in the corresponding folders.