Artificial intelligence in corticospinal tract segmentation using constrained spherical deconvolution

Tractography of cerebral white matter tracts is a technique with applications in neurosurgical planning and the diagnosis of neurological diseases. In this context, the approach based on the constrained spherical deconvolution (CSD) algorithm allows for more efficient and plausible segmentations. This study aimed to compare two CSD techniques for corticospinal tract (CST) segmentation.

This study examined 40 diffusion-weighted images (DWIs) acquired at 7T from healthy participants in the human connectome project (HCP) and 12 clinical 1.5T DWIs from patients undergoing neurosurgical procedures. Tractography was performed using two techniques: regions of interest-based approach and an automatic approach using the TractSeg neural network. The volume of the CST segmented by the two methods was compared using the Dice similarity coefficient.

There was a low similarity between the CST volumes segmented by the two techniques (Dice index for the HCP: 0.479 ± 0.04; Dice index for the Clinical: 0.404 ± 0.08). However, both techniques achieved high levels of consistency in sequential measurements, with intraclass correlation coefficient values above 0.995 for all comparisons. In addition, all selected metrics showed significant differences when comparing the two techniques (HCP - volume P < 0.0001, fractional anisotropy [FA] P = 0.0061, mean diffusivity [MD] P < 0.0001; Clinical - volume P < 0.0001, FA P = 0.0018, MD P = 0.0018).

Both methods demonstrate a high degree of consistency; however, the automatic approach appears to be more consistent overall. When comparing the CST segmentations between the two methods, we observed only a moderate similarity and differences in all considered metrics.