Hi! I was wondering how can I run a segmentation of the cerebellum with a different strategy than the one provided by the default subcortical segmentation (which only differentiates lh and rh white and grey matter). I'm currently analyzing longitudinal data from a motor learning experiment in which the cerebellum it's very important, so I would like to have more cerebellar ROIs.
I recently found an article by Makris et al., 2003, Human Cerebellum- Surface-Assisted Cortical Parcellation and Volumetry with Magnetic Resonance Imaging, but I'm not sure if there is any implementation of this strategy. Does anyone knows how can I use it? Or if there is another segmentation strategies available?

Thanks in advance

Outside of Freesurfer, one atlas that is used for parcellations is the SUIT atlas from Diedrichsen and colleagues: http://www.diedrichsenlab.org/imaging/su... I bring this atlas into subject T1 space using MATLAB and SPM for each subject and then the T1 space version can be registered to other spaces for use in cerebellar parcellation. In some lower resolution spaces, there will likely be overlap with some of the lobule masks and the Freesurfer masks, so favor whichever seems more accurate in terms of labeling. I'm not sure what analysis you will be doing, but our group has used this for structural connectivity and resting-state functional connectivity analysis. Since you are using longitudinal data, if you are doing any connectivity analysis, you should look into minimizing ROI size bias by transforming your T1s from each collection timepoint to an average (see fMRIPrep for an easy implementation of this).