BMC Medical Imaging

Table 4 Comparison of segmentation performance of different networks on different datasets

From: Scale-aware dense residual retinal vessel segmentation network with multi-output weighted loss

	DRIVE dataset				STARE dataset
network	dice	acc	mIoU	recall	dice	acc	mIoU	recall
UNet [4]	0.7751	0.9622	0.7963	0.8661	0.8103	0.9705	0.8251	0.8693
Attention U-Net [10]	0.7849	0.9644	0.8044	0.8720	0.8158	0.9718	0.8297	0.8769
CE-Net [13]	0.7794	0.9630	0.7996	0.8678	0.8141	0.9711	0.8280	0.8737
SA-Unet [5]	0.7993	0.9654	0.8120	0.8783	0.8284	0.9731	0.8395	0.8892
Sine-Net [22]	0.8006	0.9665	0.8167	0.8757	0.8303	0.9738	0.8414	0.8858
DR-Vnet [9]	0.8011	0.9665	0.8165	0.8782	0.8307	0.9733	0.8419	0.8844
CRAUnet [21]	0.8028	0.9681	0.8186	0.8763	0.8325	0.9746	0.8441	0.8855
Our network	0.8040	0.9667	0.8214	0.8810	0.8341	0.9739	0.8438	0.8884

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