Acta Optica Sinica, Volume. 38, Issue 11, 1111004(2018)
Retinal Vessel Image Segmentation Based on Improved Convolutional Neural Network
Figures & Tables(7)
Fig. 2. Schematic of dilated convolutions under different dilation rates. (a) r=1; (b) r=2; (c) r=3
Fig. 4. Segmentation results by proposed method and method in Ref. [9]. (a) Original images; (b) ground truth; (c) results in Ref. [9]; (d) results by proposed method
Fig. 5. ROC curves of segmentation results by proposed method and method in Ref. [9]. (a) ROC curves; (b) curves in red box of Fig. (a)
Table 1. Retinal vessel segmentation performance comparison among different algorithms on DRIVE datasets
View tableTable 1. Retinal vessel segmentation performance comparison among different algorithms on DRIVE datasets
Method Accuracy Sensitivity Specificity AUC Traditional method Method in Ref. [4] 0.9489 0.7657 0.9809 - Method in Ref. [5] 0.9539 0.7039 0.9783 - Method in Ref. [6] - 0.7897 0.9684 - Method in Ref. [22] 0.9457 0.7527 0.9744 - Deep learning method Method in Ref. [9] 0.9535 0.7811 0.9807 0.9765 Method in Ref. [10] 0.9533 0.7464 0.9836 0.9752 Proposed method 0.9556 0.8036 0.9778 0.9800
Table 2. [in Chinese]
View tableTable 2. [in Chinese]
Method Accuracy Sensitivity Specificity AUC Network_1 0.9537 0.7897 0.9777 0.9780 Network_2 0.9537 0.7968 0.9766 0.9772 Network_3 0.9542 0.7986 0.9768 0.9784 Network_4 0.9548 0.7973 0.9778 0.9792 Proposed 0.9556 0.8036 0.9778 0.9800
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Chenyue Wu, Benshun Yi, Yungang Zhang, Song Huang, Yu Feng. Retinal Vessel Image Segmentation Based on Improved Convolutional Neural Network[J]. Acta Optica Sinica, 2018, 38(11): 1111004
Paper Information
Category: Imaging Systems
Received: Apr. 18, 2018
Accepted: Jun. 13, 2018
Published Online: May. 9, 2019
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