Insights into Imaging

Table 2 Quantitative comparison in the presence and absence of Resblock

From: Deep learning approach for automatic segmentation of ulna and radius in dual-energy X-ray imaging

Methods	Validation set (Dice)		Testing set (Dice)
Methods	Ulna	Radius	Ulna	Radius
U-Net	0.9753 ± 0.0291	0.9832 ± 0.0126	0.9751 ± 0.0309	0.9828 ± 0.0133
with Resblock	0.9782 ± 0.0214	0.9850 ± 0.0103	0.9785 ± 0.0214	0.9852 ± 0.0100

	Validation set (Jaccard)		Testing set (Jaccard)
	Ulna	Radius	Ulna	Radius
U-Net	0.9534 ± 0.0481	0.9670 ± 0.0233	0.9532 ± 0.0488	0.9665 ± 0.0242
with Resblock	0.9581 ± 0.0368	0.9707 ± 0.0192	0.9589 ± 0.0369	0.9711 ± 0.0186

The results are expressed as the mean ± standard deviation. Bold values indicate the best score obtained for ulna and radius segmentation

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