Algorithm #1: ``sct_propseg`` ############################# The first spinal cord segmentation tool that SCT offers is called ``sct_propseg``, and it works as follows: :1. Centerline detection: ``sct_propseg`` starts by using a machine learning-based method (`OptiC `_) to automatically detect the approximate center of the spinal cord. .. figure:: https://raw.githubusercontent.com/spinalcordtoolbox/doc-figures/master/spinalcord-segmentation/optic_steps.png :align: center :figwidth: 500px (The centerline detection step is also provided in a standalone script called ``sct_get_centerline``.) :2. Mesh propagation: Next, a coarse 3D mesh is created by propagating along the spinal cord (PropSeg). .. figure:: https://raw.githubusercontent.com/spinalcordtoolbox/doc-figures/master/spinalcord-segmentation/mesh_propagation.png :align: center :figwidth: 500px :3. Surface refinement: Finally, the surface of the mesh is refined using small adjustments. ``sct_propseg`` was developed in 2014. In the years following, however, SCT has added a deep learning-based approach called ``sct_deepseg_sc``.