Table 2.

Segmentation results of cerebrovascular and airway structures by different networks (including post-processing); the evaluation index is the average value and variance of the prediction results of the test set; MSFA means multi-scale feature aggregation and the red bold is the optimal result (post-processing).

	Vessels Dataset				Airways Dataset
Network	Pre (%)	Re (%)	Di (%)	IoU (%)	Pre (%)	Re (%)	Di (%)	IoU (%)
VoxResnet [59]	85.80 ± 1.92	85.92 ± 1.78	85.24 ± 1.11	74.28 ± 1.64	93.92 ± 2.13	90.54 ± 3.12	92.32 ± 2.03	85.74 ± 2.74
APA U-Net [39]	74.41 ± 3.86	84.91 ± 1.63	79.22 ± 1.67	65.62 ± 2.26	90.65 ± 7.71	90.55 ± 7.98	91.58 ± 6.87	84.48 ± 8.57
CS2-Net [35]	93.15 ± 1.25	83.23 ± 1.20	87.90 ± 0.67	78.42 ± 1.06	91.42 ± 1.95	93.70 ± 2.01	92.54 ± 2.00	86.12 ± 2.79
ER-Net [36]	91.98 ± 1.44	84.67 ± 1.22	88.17 ± 0.57	78.84 ± 1.44	94.79 ± 2.48	90.80 ± 5.14	92.75 ± 3.53	86.49 ± 5.46
Resnet (deep = 18) [60]	91.21 ± 1.18	88.14 ± 1.66	89.63 ± 0.81	81.22 ± 1.33	96.42 ± 5.79	89.02 ± 3.27	93.27 ± 2.54	87.39 ± 3.22
U-Net [13]	91.60 ± 1.92	86.49 ± 1.03	88.95 ± 0.89	80.10 ± 1.44	96.34 ± 0.65	89.72 ± 3.15	93.25 ± 1.84	87.35 ± 3.15
Attention U-Net [17]	89.98 ± 1.35	88.48 ± 1.52	88.17 ± 0.45	78.84 ± 1.05	97.04 ± 0.61	89.86 ± 3.72	92.87 ± 2.25	86.69 ± 3.68
Rattention U-Net [50]	90.17 ± 1.39	88.30 ± 1.23	89.21 ± 0.60	80.52 ± 0.99	95.35 ± 5.21	90.55 ± 2.97	92.80 ± 3.51	86.57 ± 5.50
UARAI (Ours)	93.89 ± 1.22	87.27 ± 2.15	90.31 ± 0.82	82.33 ± 1.37	97.41 ± 0.56	89.67 ± 3.37	93.34 ± 1.98	87.51 ± 3.34