Feng Zhao, Beibei Zhong, Hanqiang Liu. Multi-Scale Residual U-Net Fundus Blood Vessel Segmentation Based on Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1810002
- Laser & Optoelectronics Progress
- Vol. 59, Issue 18, 1810002 (2022)
Fig. 1. Network structure comparison. (a) U-Net; (b) multi-scale residual U-shaped network based on attention mechanism
Fig. 2. Improved residual block structure
Fig. 3. Multi-scale convolution module
Fig. 4. Parallel dilated convolution module
Fig. 5. Multi-scale attention module
Fig. 6. Hybrid attention module
Fig. 7. Image preprocessing. (a) Original image of DRIVE dataset; (b) pre-processed image
Fig. 8. Retinal vessel segmentation results of different algorithms. (a) Original images; (b) ground truth;(c) proposed algorithm; (d) Residual U-Net[12]; (e) Recurrent U-Net[12]; (f) R2U-Net[12]; (g) algorithm in reference [28]
Fig. 9. Detail comparison of segmentation results. (a) Original image; (b) details of original images; (c) details of ground truth; (d) details of proposed algorithm; (e) details of Residual U-Net[12]; (f) details of Recurrent U-Net[12]; (g) details of R2U-Net[12]; (h) details of algorithm reference [28]
Fig. 10. Verification of role of a single module. (a) Original images; (b) Ground truth; (c) M1; (d) M2; (e) M3; (f) M4
|
Table 1. Formula of evaluation index
|
Table 2. Verification experiment of role of a single module
|
Table 3. Multi-module cumulative effect verification experiment
|
Table 4. Validation experiment of hybrid attention module
|
Table 5. DRIVE dataset fundus blood vessel segmentation results
|
Table 6. CHASE DB1 dataset fundus blood vessel segmentation results
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20