Gray matter segmentation algorithm:
For segmenting the gray matter, SCT features the function
sct_deepseg_gm, which is based on a deep learning architecture trained from 232 subjects (~4000 slices).
Algorithm: Deep learning with dilated convolutions [Perone et al., Sci Report 2018]
Pros: High accuracy, robust to pathologies
Cons: Restricted to T2*-like contrasts (GM bright, WM dark)
sct_deepseg_gm obtained the best Dice score amongst all other methods that participated in the GM challenge [Prados et al., Neuroimage 2017].