Table 2.

Segmentation evaluation quality measures comparing sparse dictionary learning–based segmentation of the cortical surface to the initial surface estimate, atlas-based segmentation, and a deep CNN^a

Method	Dice (%)	MHD (mm)	MAD (mm)	MESD (mm)
Initial estimate	93.47 ± 1.86	8.09 ± 2.50	3.76 ± 0.68	1.66 ± 0.84
Atlas-based	94.21 ± 1.77	6.81 ± 2.46	3.20 ± 0.70	2.51 ± 1.06
Deep CNN	95.00 ± 1.18	7.00 ± 2.71	2.94 ± 0.44	1.11 ± 0.61
Oriented dictionaryb	94.87 ± 1.05	6.75 ± 2.39	3.23 ± 0.43	0.87 ± 0.24

^aValues are expressed as mean ± standard deviation of voxel overlap (Dice), a modified version of the Hausdorff distance (MHD) that uses the ninety-fifth-percentile distance, mean absolute difference (MAD), and mean electrode to surface distance (MESD), which measures the distance from the surface electrode to the segmentation for the surface electrodes closest to the region of greatest clinical interest (the site of the craniotomy). Bold entries indicate the best-performing method.

^bMethodology utilizing sparsity.

Abbreviation: CNN, convolutional neural network. Table adapted from Reference 75 with permission from IEEE.