. Author manuscript; available in PMC: 2021 Dec 20.

Published in final edited form as: Med Image Comput Comput Assist Interv. 2020 Sep 29;12264:807–816. doi: 10.1007/978-3-030-59719-1_78

Table 1.

The segmentation and localization results (mean ± standard deviation) obtained by our DTNet and other three competing methods. The localization results were quantified in terms of RMSE for the 6 meta-level and all 64 landmarks, respectively. Notably, all competing methods leveraged our two-branch architecture and RDLs to deal with large input CBCT volume and large-scale landmarks.

Method	Mandible segmentation (%)			Landmark localization (mm)
Method	DSC	SEN	PPV	Meta-level	All
U-Net	92.49 ± 2.13	92.47 ± 5.13	93.32 ± 4.43	2.18 ± 0.54	2.99 ± 0.33
MTU-Net	92.34 ± 2.06	93.59 ± 4.42	91.53 ± 5.10	2.01 ± 0.55	2.85 ± 0.40
MTAN	92.66 ± 2.23	94.17 ± 4.16	91.43 ± 4.84	2.02 ± 0.51	2.91 ± 0.45
DTNet	93.95 ± 1.30	94.24 ± 1.37	93.68 ± 1.78	1.95 ± 0.43	2.52 ± 0.31