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. Author manuscript; available in PMC: 2020 Sep 1.
Published in final edited form as: Neuroimage. 2020 May 29;218:116947. doi: 10.1016/j.neuroimage.2020.116947

Table 2.

A summary of the comparison between Freesurfer 6.0, ASHS and CAST automated segmentation methods.

Automated segmentation methods Time cost to segment a new subject Feasible to train a new model? Hardware requirement to train a new model Required coregistration Support multiple modalities Adaptive to different MRI pulse sequence
Freesurfer 6.0 A few hours up to one day with -itkthreads 8 setting Not feasible Not applicable Affine and nonlinear Yes The same model is used because MRI contrast is discarded
ASHS Approximately 30 min on a 8-core machine Feasible Single workstation or computer clusters Affine and nonlinear Yes Performs best when trained on images acquired with the same sequence
CAST Less than 1 min Feasible Single workstation with a descent GPU card (e.g. Tesla K40c; GPU is not required for segmentation) Affine Yes Performs best when trained on images acquired with the same sequence