Fundus Microvascular Image Segmentation Method Based on Parallel U‐Net Model

Xinjuan Liu; Xu Han; Erxi Fang

doi:10.3788/CJL241041

Journals >Chinese Journal of Lasers >Volume 51 >Issue 21 >Page 2107108 > Article

Chinese Journal of Lasers
Vol. 51, Issue 21, 2107108 (2024)

Fundus Microvascular Image Segmentation Method Based on Parallel U‐Net Model

Xinjuan Liu, Xu Han, and Erxi Fang^*

Author Affiliations

School of Electronic and Information, Soochow University, Suzhou 215006, Jiangsu , China

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DOI: 10.3788/CJL241041 Cite this Article Set citation alerts

Xinjuan Liu, Xu Han, Erxi Fang. Fundus Microvascular Image Segmentation Method Based on Parallel U‐Net Model[J]. Chinese Journal of Lasers, 2024, 51(21): 2107108 Copy Citation Text

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Fig. 1. Overall network structure of MPU-Net

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Fig. 2. Fundus vascular and microvascular labels. (a) Ground truth; (b) microvascular label

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Fig. 3. Example of a morphological structure operator

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Fig. 4. Schematic diagram of the multi-scale feature mixing and fusion module (MSF)

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Fig. 5. Vascular segmentation dataset

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Fig. 6. Heat map examples showing the probabilistic features output by the network before and after adding MSF

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Fig. 7. Heat map examples showing the probability features output by the network before and after adding Mic-Net

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Fig. 8. Vascular segmentation examples of ablation experiment on the DRIVE test set

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Fig. 9. Vascular segmentation examples of ablation experiment on the CHASE_DB1 test set

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Fig. 10. Vascular segmentation examples of ablation experiment on the STARE test set

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Method	Year	Accuracy	Sensitivity	Specificity	AUC
U-Net^［6］	2015	0.9531	0.7537	0.9820	0.9755
Yan et al.^［20］	2019	0.9538	0.7631	0.9820	0.9750
DG-Net^［10］	2020	0.9604	0.7614	0.9837	0.9846
CS2-Net^［21］	2021	0.9553	0.8154	0.9757	0.9784
ACCA-MLA-D-U-Net^［12］	2022	0.9581	0.8046	0.9850	0.9827
Mao et al.^［14］	2023		0.8105	0.9785	0.9812
TDCAU-Net^［16］	2024	0.9556	0.8187	0.9756	0.9795
MPU-Net （ours）	2024	0.9710	0.8243	0.9853	0.9889

Table 1. Experimental results comparison of MPU-Net with existing state-of-the-art methods on the DRIVE test set

Method	Year	Accuracy	Sensitivity	Specificity	AUC
U-Net^［6］	2015	0.9578	0.8228	0.9701	0.9772
Vessel-Net^［22］	2019	0.9661	0.8132	0.9814	0.9860
CS2-Net^［21］	2021	0.9651	0.8329	0.9784	0.9851
ACCA-MLA-D-U-Net^［12］	2022	0.9673	0.8402	0.9801	0.9874
WA-Net^［23］	2022	0.9653	0.8042	0.9826	0.9841
Mao et al.^［14］	2023		0.8241	0.9850	0.9893
TDCAU-Net^［16］	2024	0.9738	0.8243	0.9836	0.9878
MPU-Net （ours）	2024	0.9764	0.8593	0.9844	0.9913

Table 2. Experimental results comparison of MPU-Net with existing state-of-the-art methods on the CHASE_DB1 test set

Method	Year	Accuracy	Sensitivity	Specificity	AUC
Yan et al.^［24］	2018	0.9612	0.7581	0.9846	0.9801
Yan et al.^［20］	2019	0.9638	0.7735	0.9857	0.9833
CS2-Net^［21］	2021	0.9670	0.8396	0.9813	0.9875
ACCA-MLA-D-U-Net^［12］	2022	0.9665	0.7914	0.9870	0.9864
LUVS-Net^［25］	2023	0.9753	0.8133	0.9861	0.8187
MPU-Net （ours）	2024	0.9768	0.7844	0.9907	0.9905

Table 3. Experimental results comparison of MPU-Net with existing state-of-the-art methods on the STARE test set

Dataset	Method	MSF	Mic-Net	Accuracy	Sensitivity	Specificity	Dice coefficient	AUC	Sen_Mic
DRIVE	1	×	×	0.9672± 0.0002	0.7928± 0.0073	0.9842± 0.0009	0.8082±0.0008	0.9816± 0.0010	0.6815± 0.0127
	2	√	×	0.9708 ±0.0001	0.8234 ±0.0052	0.9851± 0.0006	0.8309±0.0002	0.9887± 0.0001	0.7153± 0.0066
	3	√	√	0.9710± 0.0001	0.8243± 0.0012	0.9853± 0.0002	0.8314±0.0002	0.9889± 0.0001	0.7199± 0.0013
Dataset	Method	MSF	Mic-Net	Accuracy	Sensitivity	Specificity	Dice coefficient	AUC	Sen_Mic
CHASE_DB1	1	×	×	0.9757± 0.0001	0.8296± 0.0037	0.9855± 0.0003	0.8112±0.0006	0.9878± 0.0006	0.7528± 0.0050
	2	√	×	0.9762± 0.0002	0.8563± 0.0045	0.9843± 0.0005	0.8193±0.0042	0.9912± 0.0001	0.7833± 0.0064
	3	√	√	0.9764± 0.0002	0.8593± 0.0034	0.9844± 0.0005	0.8212±0.0032	0.9913± 0.0001	0.7874± 0.0052
Dataset	Method	MSF	Mic-Net	Accuracy	Sensitivity	Specificity	Dice coefficient	AUC	Sen_Mic
STARE	1	×	×	0.9766± 0.0005	0.7413± 0.0100	0.9931± 0.0003	0.7979±0.0056	0.9831± 0.0013	0.6066± 0.0184
	2	√	×	0.9764± 0.0003	0.7726± 0.0081	0.9911± 0.0005	0.8031±0.0030	0.9829± 0.0011	0.6345± 0.0090
	3	√	√	0.9768± 0.0002	0.7844± 0.0059	0.9907± 0.0005	0.8089±0.0016	0.9905± 0.0005	0.6388± 0.0042

Table 4. Comparison in ablation experimental results (mean ± std)

Xinjuan Liu, Xu Han, Erxi Fang. Fundus Microvascular Image Segmentation Method Based on Parallel U‐Net Model[J]. Chinese Journal of Lasers, 2024, 51(21): 2107108

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