Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1417003(2021)
Retinal Vessel Segmentation of Prematurity Infants Based on FDMU-net
Figures & Tables(10)
Fig. 4. Different models for ROC curve on each dataset. (a) DRIVE; (b) STARE; (c) Shengwei
Fig. 5. Comparison of different models for segmentation of small blood vessels of different models. (a) Original fundus image; (b) segmentation of U-net; (c) segmentation of FDMU-net
Fig. 6. Comparison of anti-interference ability of different models. (a) Original fundus image; (b) segmentation of U-net; (c) segmentation of FDMU-net
Fig. 7. Different models for segmentation on each dataset. (a) Fundus images of premature infants; (b) manual labeling;(c) segmentation results of Ref.[17]; (d) segmentation results of Ref.[18]; (e) segmentation results of FMDU-net
Table 1. Parameters of training set for each dataset
View tableTable 1. Parameters of training set for each dataset
Dataset No. of original images No. of images Original size Input size DRIVE 32 320 565×584 565×584 STARE 16 160 605×700 605×700 Shengwei 64 640 1200×1600 480×640
Table 2. Experimental comparison results on different datasets
View tableTable 2. Experimental comparison results on different datasets
Dataset Method Accuracy Sensitivity Specificity Dice AUC DRIVE U-net 0.9618 0.7709 0.9812 0.7871 0.9731 DU-net 0.9670 0.7881 0.9818 0.8043 0.9701 DMU-net 0.9671 0.8122 0.9819 0.8177 0.9764 FDMU-net 0.9675 0.8152 0.9826 0.8191 0.9808 STARE U-net 0.9627 0.8215 0.9755 0.7894 0.9731 DU-net 0.9678 0.8217 0.9782 0.7961 0.9757 DMU-net 0.9675 0.8391 0.9784 0.8003 0.9837 FDMU-net 0.9685 0.8484 0.9783 0.8011 0.9892 Shengwei U-net 0.9690 0.6203 0.9812 0.6064 0.9446 DU-net 0.9720 0.6400 0.9817 0.6232 0.9477 DMU-net 0.9727 0.6594 0.9833 0.6279 0.9590 FDMU-net 0.9728 0.6633 0.9840 0.6302 0.9595
Table 3. Performance comparison of FDMU-net and other methods
View tableTable 3. Performance comparison of FDMU-net and other methods
Dataset Method Year Accuracy Sensitivity Specificity AUC DRIVE Ref. [17] 2019 0.9546 0.8053 0.9767 0.9771 Ref. [18] 2020 0.9562 0.7823 0.9815 0.9793 FDMU-net 0.9675 0.8152 0.9826 0.9808 STARE Ref. [17] 2019 0.9684 0.8299 0.9794 0.9817 Ref. [18] 2020 0.9617 0.8217 0.9766 0.9854 FDMU-net 0.9685 0.8484 0.9783 0.9892 Shengwei Ref. [17] 2019 0.9697 0.6255 0.9831 0.9447 Ref. [18] 2020 0.9717 0.6337 0.9786 0.9503 FDMU-net 0.9728 0.6633 0.9840 0.9595
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Liang Wang, Chunxiao Chen, Xue Fu, Lin Wang. Retinal Vessel Segmentation of Prematurity Infants Based on FDMU-net[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1417003
Paper Information
Category: Medical Optics and Biotechnology
Received: Aug. 7, 2020
Accepted: Sep. 30, 2020
Published Online: Jul. 14, 2021
The Author Email: Chunxiao Chen (ccxbme@nuaa.edu.cn)
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