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. Author manuscript; available in PMC: 2021 Mar 26.

Published in final edited form as: IEEE Access. 2020 Nov 25;8:217603–217614. doi: 10.1109/access.2020.3040616

TABLE I.

Carotid vessel wall segmentation performance compared with other methods

Model	DSC^VW	DSC^Inner	DSC^outer	DoS^Lumen	DoS^wall	Number of slices with failed segmentation	Processing time (s)	Number of parameters in network
Polar-Res-Reg	0.860	0.961	0.962	0.921	0.864	0	0.757	44,989,224
Polar-Res-Reg-Single	0.841	0.955	0.954	0.901	0.838	0	0.891	44,989,224
Polar-Reg	0.848	0.957	0.959	0.907	0.843	0	0.738	4,639,104
Cart-Reg	0.828	0.950	0.952	0.883	0.800	0	0.092	4,639,104
Cart-Cart-Reg	0.807	0.943	0.943	0.841	0.738	0	0.080	6,212,480
Mask R-CNN [40]	0.792	0.940	0.940	0.654	0.565	81	0.138	63,733,406
Cartesian U-net [24]	0.774	0.922	0.941	0.647	0.517	194	0.103	4,094,817
Opfront [14]	0.531	0.822	0.878	N/A	N/A	N/A	38.717	N/A

Segmentations are all based on ground truth lumen centers. Slices which failed in finding ring shape contours were considered failed segmentation, and were excluded from evaluations. The metric with the best performance is shown in bold.