. Author manuscript; available in PMC: 2021 Jul 27.

Published in final edited form as: IEEE Trans Med Imaging. 2019 Dec 27;39(6):1988–1999. doi: 10.1109/TMI.2019.2962786

Table III:

PERFORMANCE COMPARISON OF THE PROPOSED FRAMEWORK AGAINST MODEL-BASED SSOP AND OTHER DEEP LEARNING ARCHITECTURES WHEN TESTED ON PROFILE-UNCORRECTED DATA (N₁). PERFORMANCE IS MEASURED IN TERMS OF NORMALIZED MEAN ABSOLUTE ERROR (NMAE)

Data type	SSOP		ResNet		UNet		ResNet-UNet		ResNet GAN		UNet GAN		Proposed (ResNet-UNet GAN)
Data type	*μ_a*	$μ_{s}^{'}$	*μ_a*	$μ_{s}^{'}$	*μ_a*	$μ_{s}^{'}$	*μ_a*	$μ_{s}^{'}$	*μ_a*	$μ_{s}^{'}$	*μ_a*	$μ_{s}^{'}$	*μ_a*	$μ_{s}^{'}$
Human esophagus	0.312	0.298	0.192	0.136	0.144	0.136	0.185	0.129	0.201	0.140	0.148	0.143	0.124	0.121
In vivo pig colon	0.171	0.112	2.032	0.145	0.251	0.186	1.533	0.145	1.953	0.133	0.190	0.152	0.074	0.067
Ex vivo pig GI tissue	0.246	0.235	0.516	0.415	0.208	0.187	0.392	0.337	0.511	0.564	0.187	0.171	0.143	0.133
In vivo hands and feet	0.194	0.101	0.337	0.070	0.100	0.066	0.250	0.068	0.643	0.162	0.089	0.056	0.048	0.030
Overall	0.231	0.187	0.769	0.192	0.176	0.144	0.590	0.170	0.827	0.250	0.154	0.131	0.097	0.088