Table 2.

Dice (%) and ASSD (mm) evaluation of 3D brain tumor segmentation with different training and testing methods. Tr −Aug: Training without data augmentation. Tr + Aug: Training with data augmentation. W-Net is a 2.5D network and W-Net (ASC) denotes the fusion of axial, sagittal and coronal views according to [43]. * denotes significant improvement from the baseline of single prediction in Tr −Aug and Tr + Aug respectively (p-value < 0.05). † denotes significant improvement from Tr −Aug with TTA + TTD (p-value < 0.05).

Train	Test	Dice (%)			ASSD (mm)
		WNet (ASC)	3D U-Net	V-Net	WNet (ASC)	3D U-Net	V-Net
Tr − Aug	Baseline	87.81 ± 7.27	87.26 ± 7.73	86.84 ± 8.38	2.04 ± 1.27	2.62 ± 1.48	2.86 ± 1.79
	TTD	88.14 ± 7.02	87.55 ± 7.33	87.13 ± 8.14	1.95 ± 1.20	2.55 ± 1.41	2.82 ± 1.75
	TTA	89.16 ± 6.48*	88.58 ± 6.50*	87.86 ± 6.97*	1.42 ± 0.93*	1.79 ± 1.16*	1.97 ± 1.40*
	TTA + TTD	89.43 ± 6.14*	88.75 ± 6.34*	88.03 ± 6.56*	1.37 ± 0.89*	1.72 ± 1.23*	1.95 ± 1.31*
Tr + Aug	Baseline	88.76 ± 5.76	88.43 ± 6.67	87.44 ± 7.84	1.61 ± 1.12	1.82 ± 1.17	2.07 ± 1.46
	TTD	88.92 ± 5.73	88.52 ± 6.66	87.56 ± 7.78	1.57 ± 1.06	1.76 ± 1.14	1.99 ± 1.33
	TTA	90.07 ± 5.69*	89.41 ± 6.05*	88.38 ± 6.74*	1.13 ± 0.54*	1.45 ± 0.81	1.67 ± 0.98*
	TTA + TTD	90.35 ± 5.64*†	89.60 ± 5.95*†	88.57 ± 6.32*†	1.10 ± 0.49*	1.39 ± 0.76*†	1.62 ± 0.95*†