Acta Optica Sinica, Volume. 40, Issue 12, 1210001(2020)
AnImproved Method for Retinal Vascular Segmentation in U-Net
Figures & Tables(14)
Fig. 4. Database partial images. (a) Original image; (b) human manual segmentation figure 1; (c) human manual segmentation figure 2; (d) mask
Fig. 5. Color image sub-channel maps. (a) RGB original image; (b) red channel; (c) green channel; (d) blue channel
Fig. 6. Local sample block. (a) Training local sample block; (b) ground truth local sample block
Fig. 7. DRIVE database segmentation results. (a) Original images; (b) ground truth images; (c) segmentation result images
Fig. 8. STARE database segmentation results. (a) Original images; (b) ground truth images; (c) segmentation result images
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
Table 1. Standard formulas for evaluation parameters
View tableTable 1. Standard formulas for evaluation parameters
Assessment index Formula AC SE SP F1-score
Table 2. AttR2U-Net segmentation performance results
View tableTable 2. AttR2U-Net segmentation performance results
Database Method AC SE SP AUC DRIVE 2nd Human observer 0.9472 0.7760 0.9724 — Ours method 0.9689 0.8028 0.9865 0.9841 STARE 2nd Human observer 0.9349 0.8968 0.9384 — Ours method 0.9796 0.8227 0.9926 0.9865
Table 3. Performance comparison of different algorithms based on U-Net networks
View tableTable 3. Performance comparison of different algorithms based on U-Net networks
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 4. Performance indicators of different algorithms in the DRIVE database
View tableTable 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 0.9472 — — — 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 5. Performance indicators of different algorithms in STARE database
View tableTable 5. Performance indicators of different algorithms in STARE 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
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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
Paper Information
Category: Image Processing
Received: Feb. 10, 2020
Accepted: Mar. 23, 2020
Published Online: Jun. 3, 2020
The Author Email: Jianxia Liu (tyljx@163.com)
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