Table 6.

Performance comparison of segmentation models built upon images normalized by the robust deviation and the standard deviation (the sizes of patches used to train FCNNs were 33^*33^*3 and 65^*65^*3 respectively, n = 5, and the number of patches used to train FCNNs was 5000^*5^*20).

Dataset	Deviation	Methods	Dice			PPV			Sensitivity
			Complete	Core	Enhancing	Complete	Core	Enhancing	Complete	Core	Enhancing
Challenge	standard	FCNNs	0.73	0.69	0.67	0.61	0.60	0.61	0.94	0.86	0.75
		FCNN + CRF	0.84	0.80	0.71	0.87	0.79	0.68	0.82	0.82	0.75
		FCNN + CRF + post-process	0.86	0.83	0.76	0.93	0.86	0.80	0.81	0.81	0.74
	robust	FCNNs	0.74	0.72	0.67	0.62	0.63	0.60	0.94	0.86	0.77
		FCNN + CRF	0.85	0.80	0.70	0.87	0.80	0.63	0.84	0.81	0.80
		FCNN + CRF + post-process	0.87	0.83	0.76	0.92	0.87	0.77	0.83	0.81	0.77
Learderboard	standard	FCNNs	0.69	0.60	0.54	0.57	0.55	0.50	0.97	0.75	0.67
		FCNN + CRF	0.83	0.66	0.58	0.85	0.71	0.56	0.85	0.70	0.67
		FCNN + CRF + post-process	0.86	0.73	0.61	0.89	0.75	0.66	0.84	0.78	0.66
	robust	FCNNs	0.70	0.61	0.54	0.58	0.57	0.49	0.96	0.74	0.67
		FCNN + CRF	0.83	0.66	0.57	0.85	0.71	0.50	0.85	0.69	0.71
		FCNN + CRF + post-process	0.86	0.73	0.62	0.89	0.76	0.64	0.84	0.78	0.68