Acta Optica Sinica, Volume. 40, Issue 4, 0410002(2020)
Retinal Vessel Segmentation Method Based on Two-Stream Networks
Figures & Tables(10)
Fig. 2. Input image and corresponding ground truth in training stage. (a) Input images; (b) ground truth for training WholeSegmentNet; (c) ground truth for training ThinSegmentNet (dark color area in picture)
Fig. 4. Results of database partitioning of DRIVE, STARE and CHASE_DB1. (a) Original images; (b) ground truth; (c) segmented whole vessel images; (d) segmented small vessel images; (e) fusion results; (f) results of proposed method
Fig. 6. Effect of ThinSegmentNet segmentation and post-processing on segmentation results. (a) Ground truth; (b) vessel images of WholeSegmentNet predictions; (c) vessel images of WholeSegmentNet+ThinSegmentNet predictions; (d) results of proposed method
Fig. 7. Segmentation of vessels in different areas. (a) Segmentation of vessels in pathological areas; (b) segmentation of vessels in central line reflex areas; (c) segmentation of vessels in low contrast areas
Table 1. Evaluation results of small vessel segmentation and post-processing methods in DRIVE, STARE and CHASE_DB1 test sets
View tableTable 1. Evaluation results of small vessel segmentation and post-processing methods in DRIVE, STARE and CHASE_DB1 test sets
Database Method Rse Rsp RAcc AUC WholeSegmentNet 0.7578 0.9816 0.9531 0.9732 DRIVE Whole+ThinSegmentNet 0.7971 0.9736 0.9522 0.9702 Proposed method 0.8062 0.9769 0.9547 0.9739 WholeSegmentNet 0.7719 0.9828 0.9610 0.9766 STARE Whole+ThinSegmentNet 0.8095 0.9757 0.9586 0.9737 Proposed method 0.8308 0.9784 0.9593 0.9788 WholeSegmentNet 0.7689 0.9801 0.9609 0.9778 CHASE_DB1 Whole+ThinSegmentNet 0.7997 0.9731 0.9601 0.9764 Proposed method 0.8135 0.9762 0.9617 0.9782
Table 2. Comparison of retinal vessel segmentation results among different methods on DRIVE, STARE and CHASE_DB1 databases
View tableTable 2. Comparison of retinal vessel segmentation results among different methods on DRIVE, STARE and CHASE_DB1 databases
Method DRIVE STARE CHASE_DB1 Rse Rsp RAcc AUC Rse Rsp RAcc AUC Rse Rsp RAcc AUC Ref. [22] 0.7395 0.9782 0.9494 0.9672 0.7317 0.9842 0.956 0.9673 0.7615 0.9575 0.9467 0.9623 Ref. [5] 0.7655 0.9704 0.9442 0.9614 0.7716 0.9701 0.9497 0.9563 0.7585 0.9587 0.9387 0.9487 Ref. [23] 0.7420 0.9820 0.9540 0.8620 0.7800 0.9780 0.9560 0.8740 Ref. [8] 0.7743 0.9725 0.9476 0.9636 0.7791 0.9758 0.9554 0.9748 0.7626 0.9661 0.9452 0.9606 Ref. [9] 0.7569 0.9816 0.9527 0.9738 0.7726 0.9844 0.9628 0.9879 0.7507 0.9793 0.9581 0.9716 Ref. [10] 0.7897 0.9684 0.768 0.9738 0.7277 0.9712 Ref. [24] 0.7691 0.9801 0.9533 0.9744 Ref. [14] 0.7653 0.9818 0.9542 0.9752 0.7581 0.9846 0.9612 0.9801 0.7633 0.9809 0.9610 0.9781 Proposed method 0.8062 0.9769 0.9547 0.9739 0.8308 0.9784 0.9593 0.9788 0.8135 0.9762 0.9617 0.9782
Table 3. Evaluation results of 10 pathological images in STARE database
View tableTable 3. Evaluation results of 10 pathological images in STARE database
Method Rse Rsp RAcc Ref. [18] 0.6587 0.9565 0.9258 Ref. [25] 0.7262 0.9764 0.9511 Ref. [9] 0.7800 0.9805 0.9672 Proposed method 0.8137 0.9758 0.9609
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Xiaowen Lü, Feng Shao, Yiming Xiong, Weishan Yang. Retinal Vessel Segmentation Method Based on Two-Stream Networks[J]. Acta Optica Sinica, 2020, 40(4): 0410002
Paper Information
Category: Image Processing
Received: Jul. 8, 2019
Accepted: Nov. 6, 2019
Published Online: Feb. 11, 2020
The Author Email: Shao Feng (shaofeng@nbu.edu.cn)
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