Table 3. Performance comparison with convolutional and transformer baselines.

Method	Dataset A					Dataset B
	DSC	AER	HE	MAE		DSC	AER	HE	MAE
UNet	0.730±0.008	0.632±0.036	97.75±1.87	54.99±1.37		0.840±0.007	0.378±0.032	53.91±2.55	24.60±1.57
UNet++	0.704±0.008	0.702±0.037	102.48±1.93	65.18±1.49		0.849±0.006	0.310±0.019	83.89±3.07	42.83±1.94
Att UNet	0.703±0.008	0.654±0.032	92.25±1.99	44.23±1.35		0.872±0.005	0.245±0.010	72.05±2.93	33.30±1.71
RDAU	0.770±0.008	0.537±0.036	80.96±2.02	34.93±1.17		0.885±0.005	0.223±0.010	54.62±2.35	21.17±1.11
Ghost UNet	0.711±0.009	0.604±0.032	89.92±1.97	51.05±1.42		0.884±0.006	0.207±0.007	39.98±2.22	13.14±0.89
SGUNet	0.782±0.007	0.499±0.030	94.25±2.03	55.33±1.49		0.898±0.004	0.195±0.007	66.35±2.83	28.40±1.47
MedT	0.729±0.008	0.556±0.024	90.10±1.83	51.65±1.34		0.889±0.005	0.219±0.010	45.74±2.33	17.71±1.19
DT	0.769±0.007	0.588±0.036	83.50±1.99	35.74±1.18		0.889±0.004	0.224±0.009	38.89±2.03	14.48±1.05
UNeXt	0.769±0.008	0.546±0.037	80.67±2.09	35.11±1.23		0.902±0.004	0.189±0.007	29.58±1.76	10.71±0.91
ConvUNeXt	0.792±0.008	0.481±0.037	81.94±2.17	39.07±1.37		0.909±0.006	0.163±0.007	27.54±1.84	9.18±0.88
GDUNet	0.806±0.007*	0.477±0.036*	73.77±2.08*	28.46±1.05*		0.925±0.004*	0.144±0.005*	17.57±1.05*	4.65±0.41*

The data in the table are presented as the mean ± standard deviation. *, best result in the table. DSC, Dice similarity coefficient; AER, area error ratio; HE, Hausdorff error; MAE, mean absolute error; UNet, U-shaped network; Att UNet, attention UNet; RDAU, Residual-Dilated-Attention-Gate-UNet; Ghost UNet, Ghost-Net and U-Net; SGUNet, semantic-guided UNet; MedT, medical transformer; DT, dilate transformer; GDUNet, attention gate and dilation U-shaped network.