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
- Acta Optica Sinica
- Vol. 40, Issue 4, 0410002 (2020)
Fig. 1. Overall framework of proposed method
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. 3. Residual network structure
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. 5. ROC curves with different databases. (a) DRIVE; (b) STARE; (c) CHASE_DB1
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
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Table 1. Evaluation results of small vessel segmentation and post-processing methods in DRIVE, STARE and CHASE_DB1 test sets
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Table 2. Comparison of retinal vessel segmentation results among different methods on DRIVE, STARE and CHASE_DB1 databases
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Table 3. Evaluation results of 10 pathological images in STARE database
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