. Author manuscript; available in PMC: 2020 Apr 1.

Published in final edited form as: IEEE Trans Med Imaging. 2018 Oct 12;38(4):919–931. doi: 10.1109/TMI.2018.2875814

TABLE VI:

Segmentation performances in different datasets, training paradigms (mixed vs. sequential), and post-processing algorithms.

	Post-processing	DSC(%)	Sensitivity(%)	Specificity(%)	HD(mm)
Sequential Tiramisu Segmentation MICCAI 2015	-	92.30	86.43	99.96	5.09
Sequential Tiramisu Segmentation MICCAI 2015	connected component analysis, 3D fill	93.86	95.23	99.99	4.58
Sequential Tiramisu Segmentation NIH Dataset	-	92.61	93.42	99.97	8.80
Sequential Tiramisu Segmentation NIH Dataset	connected component analysis, 3D fill	93.82	93.42	99.97	6.36
Mixed Tiramisu Segmentation → U-Net Landmark Localization NIH Dataset	-	92.09	92.10	99.96	8.30
	connected component analysis, 3D fill	92.28	92.10	99.96	7.11
Mixed Tiramisu Segmentation → Tiramisu Landmark Localization NIH Dataset	-	90.10	90.53	99.97	8.80
	Connected component analysis, 3D fill	90.10	90.52	99.97	6.36