Gray matter segmentation algorithm: ``sct_deepseg_gm``
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.. figure:: https://raw.githubusercontent.com/spinalcordtoolbox/doc-figures/master/gm-wm-segmentation/gm-challenge.png
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   Results of the GM Challenge

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] <https://www.nature.com/articles/s41598-018-24304-3>`_
* **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] <https://pubmed.ncbi.nlm.nih.gov/28286318/>`_.