Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1617002(2021)
Two-Stage Retinal Vessel Segmentation Based on Improved U-Net
Figures & Tables(17)
Fig. 7. Fundus image channel comparison maps. (a) RGB original image; (b) red channel; (c) green channel; (d) blue channel
Fig. 8. Image preprocessing. (a) Original color image; (b) original image after extracting the green channel; (c) image after CLAHE operation; (d) after rotating 90°; (e) after rotating 180°; (f) after rotating 270°; (g) after horizontal flip; (h) after vertical flip
Fig. 9. Segmentation results of proposed method on DRIVE database. (a) Original images; (b) ground truth images; (c) segmentation images
Fig. 10. Segmentation results of proposed method on STARE database. (a) Original images; (b) ground truth images; (c) segmentation images
Fig. 11. Local segmentation maps. (a) Original fundus images; (b) partial color fundus maps; (c) standard partial segmentation maps; (d) ours local segmentation maps
Table 1. Performance comparison of different segmentation methods based on U-Net network
View tableTable 1. Performance comparison of different segmentation methods based on U-Net network
Method Precision Recall F1-Score Accuracy M1 0.8475 0.8270 0.8373 0.9690 M2 0.8443 0.8341 0.8392 0.9731 M3 0.8524 0.8469 0.8497 0.9743 M4 0.8563 0.8639 0.8609 0.9787
Table 2. Performance indicators of different methods in DRIVE database
View tableTable 2. Performance indicators of different methods in DRIVE database
Method Year Precision Recall F1-Score Accuracy U-Net[ 25 ]2018 0.8852 0.7537 0.8142 0.9531 Residual U-Net[ 25 ]2018 0.8614 0.7726 0.8149 0.9553 Recurrent U-Net[ 25 ]2018 0.8603 0.7751 0.8155 0.9556 R2 U-Net[25] 2018 0.8589 0.7792 0.8171 0.9556 Conditional GAN[ 20 ]2018 0.8143 0.8274 0.8208 0.9608 LadderNet[ 21 ]2018 0.8593 0.7856 0.8208 0.9561 DUNet[ 22 ]2019 0.8529 0.7963 0.8237 0.9566 Dynamic Deep Networks[ 19 ]2019 0.8284 0.8235 0.8259 0.9693 Ours 2020 0.8331 0.8369 0.8351 0.9698
Table 3. Performance indicators of different methods in STARE database
View tableTable 3. Performance indicators of different methods in STARE database
Method Year Precision Recall F1-Score Accuracy U-Net[ 25 ]2018 0.8475 0.8270 0.8373 0.9690 Residual U-Net[ 25 ]2018 0.8581 0.8203 0.8388 0.9700 Recurrent U-Net[ 25 ]2018 0.8705 0.8108 0.8396 0.9706 R2 U-Net[ 25 ]2018 0.8659 0.8298 0.8475 0.9712 Conditional GAN[ 20 ]2018 0.8466 0.8538 0.8502 0.9771 DUNet[ 22 ]2019 0.8777 0.7595 0.8143 0.9641 Dynamic Deep Networks[ 19 ]2019 0.8559 0.8541 0.8549 0.9780 Ours 2020 0.8563 0.8639 0.8609 0.9787
Table 4. Comparison of inference time of different methods on two databases
View tableTable 4. Comparison of inference time of different methods on two databases
Method Platform Inference time /ms DRIVE STARE U-Net[ 25 ]NVIDIA GTX 1080Ti 18 17 Residual U-Net[ 25 ]NVIDIA GTX 1080Ti 19 17 R2 U-Net[ 25 ]NVIDIA GTX 1080Ti 17 15 Ours NVIDIA GTX 1080Ti 16 14
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Qianhong Cai, Yuhong Liu, Rongfen Zhang. Two-Stage Retinal Vessel Segmentation Based on Improved U-Net[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1617002
Paper Information
Category: Medical Optics and Biotechnology
Received: Aug. 10, 2020
Accepted: Dec. 17, 2020
Published Online: Aug. 16, 2021
The Author Email: Rongfen Zhang (rfzhang@gzu.edu.cn)
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