动态图谱融合Transformer视网膜血管分割算法

doi:10.19665/j.issn1001-2400.20241204

Abstract

Abstract:

To address the issues of excessive loss of vascular features at the encoding end,poor segmentation ability of vascular regions in lesion areas,and insufficient extraction of global contextual information in existing algorithms,this paper proposes a dynamic feature weighting and graph reasoning Transformer retinal vessel segmentation algorithm.First,an adaptive weighting encoding end is designed to alleviate the problem of vascular loss caused by continuous convolution and downsampling,thus enhancing vascular texture features.Second,a graph reasoning Transformer module is constructed to simultaneously extract pixel-level vascular features and relationships between nodes,thereby effectively capturing global and local information in image data.The final construction of the dynamic feature enhancement module at the decoder side and the encoder-decoder base effectively improves the ability to segment vascular lesions.Experimental results on DRIVE,CHASE-DB1 and STARE datasets show that the proposed algorithm exhibits a superior segmentation performance and generalization ability with only 0.91M model parameters,with accuracies of 97.01%,97.37%,and 97.42%,sensitivities of 82.51%,84.47%,and 81.21%,and AUC-ROC of 98.74%,98.83%,and 98.94%,respectively,showing a certain clinical application value in the diagnosis of ophthalmic diseases.

Key words: retinal vessel segmentation, graph convolution, Transformer, adaptive weighted downsampling, dynamic feature enhancement

CLC Number:

TP391

LIANG Liming, LU Baohe, LONG Pengwei, JIN Jiaxin, WU Jian. Dynamic graph reasoning transformer retinal vessel segmentation algorithm[J].Journal of Xidian University, 2025, 52(2): 85-100.

Figures/Tables 26

References 33

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数据集	方法	准确率	灵敏度	特异性	F1	AUC
	U-Net^[9]	96.97	79.22	98.74	82.40	98.68
	Vlight^[21]	96.73	73.81	98.93	79.82	98.28
DRIVE	FR U-Net^[22]	97.01	79.62	98.68	82.35	98.69
	GT-DLA-dsHFF^[23]	97.10	78.96	98.84	82.68	98.71
	FMVG-Net^[24]	97.08	79.98	98.72	82.76	98.65
	DGRT-Net(文中)	97.01	82.51	98.43	82.86	98.74

数据集	方法	准确率	灵敏度	特异性	F1	AUC
	U-Net^[9]	97.32	79.32	98.72	80.00	98.74
	Vlight^[21]	96.37	75.76	98.83	78.46	98.53
CHASE-DB1	FR U-Net^[22]	97.43	79.57	98.63	79.62	98.67
	GT-DLA-dsHFF^[23]	97.25	83.59	98.17	79.32	98.71
	FMVG-Net^[24]	97.49	79.50	98.70	80.01	98.75
	DGRT-Net(文中)	97.37	84.47	98.25	80.22	98.83

数据集	方法	准确率	灵敏度	特异性	F1	AUC
	U-Net^[9]	97.42	79.40	98.98	82.67	98.92
	Vlight^[21]	97.19	76.07	98.93	80.49	98.71
STARE	FR U-Net^[22]	97.45	80.98	98.77	82.72	98.99
	GT-DLA-dsHFF^[23]	97.50	79.30	98.99	82.81	99.01
	FMVG-Net^[24]	97.45	79.86	98.89	82.66	98.99
	DGRT-Net(文中)	97.42	81.21	98.79	82.89	98.94

数据集	方法	准确率	灵敏度	特异性	F1	AUC-ROC
	NAUNet^[25]	96.71	79.99	98.31		98.31
	IterMiUnet^[26]	95.68	80.53	97.89	82.62	98.10
	RFENet^[27]	95.76	80.17	98.12		98.16
	DPN^[28]	95.71	79.34	98.10	82.89	98.16
	ResDO-UNet^[10]	95.61	79.85	97.91	82.29
DRIVE	FRS-Net^[29]	95.74	79.97	98.04	82.69	98.16
	Bridge-Net^[30]	95.65	78.53	98.18	82.03	98.34
	DEF-Net^[31]	95.56	81.38	97.63	82.36	97.89
	Wave-Net^[32]	95.61	81.64	97.64	82.54
	STSANet^[33]	95.60	78.90	98.04		97.94
	DGRT-Net(文中)	97.01	82.51	98.43	82.86	98.74

数据集	方法	准确率	灵敏度	特异性	F1	AUC-ROC
	NAUNet^[25]	96.83	80.71	98.20		98.41
	IterMiUnet^[26]	95.91	84.43	97.04	78.75	98.12
	DPN^[28]	96.50	76.45	98.46	80.21	98.40
	ResDO-UNet^[10]	96.72	80.20	97.94	82.36
CHASE-DB1	FRS-Net^[29]	96.71	81.55	98.22	81.79	98.75
	Bridge-Net^[30]	96.67	81.32	98.40	82.93	98.93
	DEF-Net^[31]	96.26	80.53	98.35	80.76	98.57
	Wave-Net^[32]	96.64	82.84	98.21	83.49
	STSANet^[33]	96.49	79.74	98.20		98.48
	DGRT-Net(文中)	97.37	84.47	98.25	80.22	98.83

Dynamic graph reasoning transformer retinal vessel segmentation algorithm

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数据集	方法	准确率	灵敏度	特异性	F1	AUC-ROC
	NAUNet^[25]	96.79	79.51	98.45		98.49
	IterMiUnet^[26]	96.49	80.69	98.31	82.31	98.52
	RFENet^[27]	96.38	79.03	98.37		98.20
	ResDO-UNet^[10]	95.67	79.63	97.92	81.72
STARE	FRS-Net^[29]	97.30	80.13	98.93	83.71	99.14
	Bridge-Net^[30]	96.68	80.02	98.64	82.89	99.01
	DEF-Net^[31]	96.07	79.58	98.15	81.86	98.38
	Wave-Net^[32]	96.41	79.02	98.36	81.40
	STSANet^[33]	96.21	72.92	98.98		98.18
	DGRT-Net(文中)	97.42	81.21	98.79	82.89	98.94

数据集	加权因子	准确率	灵敏度	特异性	F1	AUC
	0.1	97.00	82.29	98.41	82.80	98.69
	0.2	97.01	82.22	98.43	82.84	98.71
	0.3	96.97	83.46	98.27	82.86	98.71
	0.4	96.92	82.43	98.31	82.44	98.58
DRIVE	0.5	96.95	83.16	98.28	82.72	98.67
	0.6	97.00	82.88	98.35	82.87	98.72
	0.7	97.00	82.90	98.35	82.88	98.73
	0.8	97.01	82.51	98.43	82.86	98.74
	0.9	96.99	82.06	98.42	82.69	98.70

数据集	加权因子	准确率	灵敏度	特异性	F1	AUC
	0.1	97.07	88.38	97.66	79.21	98.79
	0.2	97.33	84.72	98.18	80.04	98.76
	0.3	97.25	86.24	97.99	79.85	98.80
	0.4	97.26	85.75	98.03	79.79	98.78
CHASE-DB1	0.5	97.31	85.13	98.12	79.95	98.80
	0.6	97.32	84.82	98.16	79.95	98.77
	0.7	97.32	85.37	98.12	80.07	98.77
	0.8	97.37	84.47	98.25	80.22	98.83
	0.9	97.24	85.97	98.01	79.70	98.78

算法	Params/M	FLOP/G
U-Net^[9]	34.52	4.090
Vlight^[21]	0.74	0.037
FR U-Net^[22]	7.37	3.680
GT-DLA-dsHFF^[23]	29.82	14.830
FMVG-Net^[24]	3.24	9.380
DGRT-Net(ours)	0.91	2.570

实验方法	方法	准确率	灵敏度	特异性	F1	AUC
	U-Net^[9]	97.28	79.07	98.52	78.79	98.14
	Vlight^[21]	96.98	77.40	98.81	76.59	98.22
①	FR U-Net^[22]	97.17	82.30	98.18	78.80	98.62
	GT-DLA-dsHFF^[23]	96.92	85.28	97.64	78.15	98.74
	FMVG-Net^[24]	96.87	81.68	97.90	76.92	98.35
	DGRT-Net(文中)	97.10	85.60	97.89	79.06	98.75

实验方法	方法	准确率	灵敏度	特异性	F1	AUC
	U-Net^[9]	96.18	71.55	98.56	72.37	98.02
	Vlight^[21]	96.53	67.92	98.50	71.64	97.20
②	FR U-Net^[22]	96.69	76.14	98.10	74.78	97.85
	GT-DLA-dsHFF^[23]	96.65	65.86	98.78	71.76	98.18
	FMVG-Net^[24]	96.35	57.71	99.01	67.10	97.59
	DGRT-Net(文中)	96.61	79.20	97.81	75.08	97.91

实验方法	方法	准确率	灵敏度	特异性	F1	AUC
	U-Net^[9]	96.77	72.40	99.01	79.37	98.46
	Vlight^[21]	96.28	66.73	99.04	75.40	97.85
③	FR U-Net^[22]	96.32	63.65	99.38	74.80	98.22
	GT-DLA-dsHFF^[23]	96.93	74.69	98.83	80.11	98.59
	FMVG-Net^[24]	96.79	75.09	98.82	80.05	98.18
	DGRT-Net(文中)	96.74	75.88	98.70	80.26	98.29

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算法	训练时间/s	测试时间/s
U-Net^[9]	79	165
Vlight^[21]	45	63
FR U-Net^[22]	139	203
GT-DLA-dsHFF^[23]	278	3 186
FMVG-Net^[24]	159	434
DGRT-Net(ours)	73	153

数据集	GRT	AWD	DFE	准确率	灵敏度	特异性	F1	AUC
				97.32	79.32	98.72	80.00	98.74
	√			97.15	86.47	97.87	79.31	98.77
		√		97.53	77.98	98.84	79.92	98.74
CHASE-DB1			√	97.21	87.31	97.88	79.83	98.68
	√	√		97.27	85.60	98.06	79.84	98.78
	√		√	96.66	90.64	97.07	77.42	98.72
		√	√	97.15	86.80	97.85	79.38	98.79
	√	√	√	97.37	84.47	98.25	80.22	98.83