AnImproved Method for Retinal Vascular Segmentation in U-Net

Wenxuan Xue; Jianxia Liu; Ran Liu; Xiaohui Yuan

doi:10.3788/AOS202040.1210001

Journals >Acta Optica Sinica >Volume 40 >Issue 12 >Page 1210001 > Article

Acta Optica Sinica
Vol. 40, Issue 12, 1210001 (2020)

AnImproved Method for Retinal Vascular Segmentation in U-Net

Wenxuan Xue¹, Jianxia Liu^1、*, Ran Liu¹, and Xiaohui Yuan²

Author Affiliations

¹College of Information and Computer, Taiyuan University of Technology, Jinzhong, Shanxi 0 30600, China

²Computer Science Department, University of North Texas, Denton, Texas 76201, United States

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DOI: 10.3788/AOS202040.1210001 Cite this Article Set citation alerts

Wenxuan Xue, Jianxia Liu, Ran Liu, Xiaohui Yuan. AnImproved Method for Retinal Vascular Segmentation in U-Net[J]. Acta Optica Sinica, 2020, 40(12): 1210001 Copy Citation Text

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Fig. 1. Schematic of R2CU

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Fig. 2. Attention module schematic

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Fig. 3. AttR2U-Net network structure diagram

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Fig. 4. Database partial images. (a) Original image; (b) human manual segmentation figure 1; (c) human manual segmentation figure 2; (d) mask

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Fig. 5. Color image sub-channel maps. (a) RGB original image; (b) red channel; (c) green channel; (d) blue channel

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Fig. 6. Local sample block. (a) Training local sample block; (b) ground truth local sample block

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Fig. 7. DRIVE database segmentation results. (a) Original images; (b) ground truth images; (c) segmentation result images

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Fig. 8. STARE database segmentation results. (a) Original images; (b) ground truth images; (c) segmentation result images

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Fig. 9. Segmentation results. (a) Img255 image; (b) ground truth images; (c) U-Net segmentation result; (d) AttR2U-Net segmentation result; (e) img255 local image; (f) local ground truth images; (g) U-Net local segmentation results; (h) AttR2U-Net local segmentation result

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Assessment index	Formula
AC	$\frac{p_{TN} + p_{TP}}{p_{TP} + p_{FP} + p_{TN} + p_{FN}}$
SE	$\frac{p_{TP}}{p_{TP} + p_{FN}}$
SP	$\frac{p_{TN}}{p_{TN} + p_{FP}}$
F1-score	$\frac{2 p_{TP}}{2 p_{TP} + p_{FN} + p_{FP}}$

Table 1. Standard formulas for evaluation parameters

Database	Method	AC	SE	SP	AUC
DRIVE	2^nd Human observer	0.9472	0.7760	0.9724	—
DRIVE	Ours method	0.9689	0.8028	0.9865	0.9841
STARE	2^nd Human observer	0.9349	0.8968	0.9384	—
STARE	Ours method	0.9796	0.8227	0.9926	0.9865

Table 2. AttR2U-Net segmentation performance results

Method	AC	SE	SP	F1	AUC
N1	0.9533	0.7722	0.9803	0.8200	0.9782
N2	0.9563	0.7829	0.9815	0.8197	0.9776
N3	0.9587	0.7846	0.9813	0.8240	0.9819
N4	0.9689	0.8028	0.9865	0.8317	0.9841

Table 3. Performance comparison of different algorithms based on U-Net networks

Type	Method	Year	AC	SE	SP	F1-score	AUC
Unsupervised method	Lam et al.^[23]	2010	0.9472	—	—	—	0.9614
	Fraz et al.^[24]	2011	0.9430	0.7152	0.9759	—	—
	You et al.^[25]	2011	0.9434	0.7410	0.9751	—	—
	Azzopardi et al.^[26]	2015	0.9442	0.7655	0.9704	—	0.9614
Supervised method	Marín et al.^[27]	2011	0.9452	0.7067	0.9801	—	0.9558
	Fraz et al.^[11]	2012	0.9480	0.7406	0.9807	—	0.9747
	Roychowdhury et al.^[28]	2016	0.9520	0.7250	0.9830	—	0.9620
	Liskowski et al.^[29]	2016	0.9495	0.7763	0.9768	—	0.9720
	Li et al.^[30]	2016	0.9527	0.7569	0.9816	—	0.9738
	U-Net^[14]	2018	0.9531	0.7537	0.9820	0.8142	0.9755
	AttR2U-Net(ours)	2019	0.9689	0.8028	0.9865	0.8317	0.9841

Table 4. Performance indicators of different algorithms in the DRIVE database

Type	Method	Year	AC	SE	SP	F1-score	AUC
Unsupervised method	Lam et al.^[23]	2010	0.9567	—	—	—	0.9739
	Fraz et al.^[24]	2011	0.9442	0.7311	0.9680	—	—
	You et al.^[25]	2011	0.9497	0.7260	0.9756	—	—
	Azzopardi et al.^[26]	2015	0.9563	0.7716	0.9701	—	0.9497
Supervised method	Marín et al.^[27]	2011	0.9520	0.6940	0.9770	—	0.9820
	Fraz et al.^[11]	2012	0.9534	0.7548	0.9763	—	0.9768
	Roychowdhury et al.^[28]	2016	0.9510	0.7720	0.9730	—	0.9690
	Liskowski et al.^[29]	2016	0.9566	0.7867	0.9754	—	0.9785
	Li et al.^[30]	2016	0.9628	0.7726	0.9844	—	0.9879
	U-Net^[14]	2018	0.9690	0.8270	0.9842	0.8373	0.9898
	AttR2U-Net(ours)	2019	0.9796	0.8227	0.9926	0.8604	0.9865

Table 5. Performance indicators of different algorithms in STARE database

Wenxuan Xue, Jianxia Liu, Ran Liu, Xiaohui Yuan. AnImproved Method for Retinal Vascular Segmentation in U-Net[J]. Acta Optica Sinica, 2020, 40(12): 1210001

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