Table 1.

Performance comparison with state-of-the-art semi-supervised segmentation methods on the H&E image muscle fiber dataset

Label	Method	DSC (%)	IoU (%)	Precision (%)	Recall (%)	F1-score (%)
1/16 (27)	SupOnly	64.88	50.48	78.46	58.52	67.04
	MT [29]	51.16	37.05	53.29	56.08	54.64
	UA-MT [30]	68.56	57.39	72.15	69.49	70.79
	ASE-Net [31]	71.46	58.51	77.63	72.38	74.91
	MMS [32]	72.88	59.87	78.94	66.68	72.29
	Our method	74.45	61.68	79.81	72.12	75.77
1/8 (54)	SupOnly	73.28	60.32	78.68	71.97	75.18
	MT [29]	66.43	51.67	71.12	67.36	69.19
	UA-MT [30]	79.82	68.65	86.85	77.24	81.76
	ASE-Net [31]	82.98	73.15	82.69	83.71	83.19
	MMS [32]	82.63	70.09	87.65	78.27	82.69
	Our method	83.47	73.75	85.59	83.23	84.39
1/4 (108)	SupOnly	83.53	73.58	84.35	83.97	84.16
	MT [29]	73.98	61.22	82.42	70.15	75.79
	UA-MT [30]	84.56	75.09	91.18	81.36	85.99
	ASE-Net [31]	86.82	78.31	91.51	94.46	93.07
	MMS [32]	87.15	76.62	90.47	83.39	86.79
	Our method	89.26	82.13	91.54	88.69	89.77
100%	FullSup	90.14	83.27	90.25	90.35	90.29

Values in bold indicate the best experimental results