Cerebellum Sub-Segmentation (CerebNet)¶
The cerebellum module (cereb): a deep learning network (CerebNet) that divides the cerebellum into its lobules and
separates gray and white matter, and computes volume statistics for them.
What it computes¶
A sub-segmentation of the cerebellum with a detailed delineation of gray and white matter (
cerebellum.CerebNet.nii.gz).Volume statistics for the cerebellar structures, corrected for partial volume effects (
cerebellum.CerebNet.stats).
The output files are listed in the output files overview.
What it needs¶
A T1-weighted image, see the requirements to input images.
The whole-brain segmentation of the FastSurferVINN module, which locates the cerebellum.
CerebNet works at 1 mm: images with smaller voxels are resampled to 1 mm for it, and its outputs are at 1 mm.
The module adds a few minutes on a GPU to the segmentation.
Options¶
The module runs by default, as part of the segmentation. The options of run_fastsurfer.sh for it:
--no_cereb: skip this module.--cereb_segfile <path>: where to write the segmentation (defaultmri/cerebellum.CerebNet.nii.gz).--no_biasfield: skip the partial-volume corrected statistics.
All options are described in the run_fastsurfer.sh reference. To run the network script directly, see CerebNet in the command reference.
References¶
If you use the cerebellum sub-segmentation in your research, please cite:
Faber J*, Kuegler D*, Bahrami E*, et al. (*co-first) CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation. NeuroImage 264 (2022), 119703. doi:10.1016/j.neuroimage.2022.119703