Table 1. Segmentation performance metrics on two publically available retinal image datasets.

Datasets		Method	MCC	SE	SP	ACC	AUC	F1
DRIVE	Unsupervised	Zhao [29]	N/A	0.7420	0.9820	0.9540	0.8620	N/A
		Azzopardi [30]	N/A	0.7655	0.9704	0.9442	0.9614	N/A
		Roychowdhury [31]	N/A	0.7395	0.9782	0.9494	0.8672	N/A
	Supervised	U-Net [2]	N/A	0.7537	0.9820	0.9531	0.9755	0.8142
		RU-Net [6]	N/A	0.7792	0.9813	0.9556	0.9784	0.8171
		DE-Unet [25]	N/A	0.7940	0.9816	0.9567	0.9772	0.8270
		SWT-UNet [32]	0.8045	0.8039	0.9804	0.9576	0.9821	0.8281
		BTS-UNet [33]	0.7923	0.7800	0.9806	0.9551	0.9796	0.8208
		Driu [34]	0.7941	0.7855	0.9799	0.9552	0.9793	0.8220
		CS-Net [35]	N/A	0.8170	0.9854	0.9632	0.9798	N/A
		SA-Net (Ours)	0.8055	0.8252	0.9764	0.9569	0.9822	0.8289
CHASE-DB1	Unsupervised	Azzopardi [30]	N/A	0.7585	0.9587	0.9387	0.9487	N/A
		Roychowdhury [31]	N/A	0.7615	0.9575	0.9467	0.9623	N/A
	Supervised	RU-Net [6]	N/A	0.7756	0.9820	0.9634	0.9815	0.7928
		SWT-UNet [32]	0.8011	0.7779	0.9864	0.9653	0.9855	0.8188
		BTS-UNet [33]	0.7733	0.7888	0.9801	0.9627	0.9840	0.7983
		SA-Net (Ours)	0.8102	0.8199	0.9827	0.9665	0.9865	0.8280

^aN/A = Not Available, SWT-UNet shows the vessel segmentation results using fully convolutional neural networks, BTS-DSN gives the segmentation results with the multi-scale deeply-supervised networks with short connections.