GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation
Abstract
:1. Introduction
2. Related Works
Diabetic Retinopathy Retinal Vasculature Segmentation
3. Proposed Method
3.1. Data Augmentation
3.2. GAN-Based Retinal Vasculature Segmentation
4. Experimental Results
4.1. Datasets
4.2. Evaluation Metrics
4.3. Evaluation and Discussion
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Study | Method | Dataset(s) | Year |
---|---|---|---|
Gargari et al. [17] | U-Net++ | DRIVE, MESSIDOR | 2022 |
Roychowdhury et al. [11] | Gaussian mixture model classifier | DRIVE, CHASEDB1, STARE | 2014 |
Fan et al. [10] | Image matting | DRIVE, CHASEDB1, STARE | 2018 |
Memari et al. [9] | Fuzzy c means clustering | DRIVE, CHASEDB1, STARE | 2019 |
Zhang et al. [22] | U-Net | DRIVE, CHASE-DB1, STARE, HRF | 2022 |
Atli and Gedik [16] | Sine-Net | DRIVE, CHASEDB1, STARE | 2021 |
Sathananthavathi et al. [18] | U-Net | CHASE DB1, DRIVE, STARE, HRF | 2021 |
Deng and Ye [26] | D-MNet | CHASE DB1, DRIVE, STARE, HRF | 2022 |
Elaouaber et al. [20] | Multiple DL models | DRIVE, CHASE-DB1, HRF | 2022 |
Dataset | Method | Accuracy | Sensitivity | Specificity | Dice | Jaccard | MCC | Precision |
---|---|---|---|---|---|---|---|---|
DRIVE | Kar et al. [41] | 0.974 | 0.894 | 0.988 | - | - | - | 0.875 |
Elaouaber et al. [20] | 0.977 | 0.967 | 0.996 | 0.957 | - | - | - | |
Zhang et al. [22] | 0.957 | 0.785 | 0.982 | 0.82 | - | 0.798 | 0.864 | |
Popescu et al. [33] | 0.921 | 0.834 | 0.960 | - | - | - | 0.948 | |
Yue et al. [34] | 0.970 | 0.833 | 0.985 | - | - | - | - | |
Park et al. [35] | 0.970 | 0.834 | 0.983 | - | - | 0.826 | 0.834 | |
Proposed | 0.978 | 0.975 | 0.981 | 0.978 | 0.956 | 0.956 | 0.98 | |
HRF | Kar et al. [41] | 0.977 | 0.889 | 0.985 | - | - | - | 0.8 |
Elaouaber et al. [20] | 0.98 | 0.98 | 0.995 | 0.969 | - | - | - | |
Zhang et al. [22] | 0.96 | 0.85 | 0.971 | 0.82 | - | - | - | |
Park et al. [35] | 0.967 | - | - | - | - | 0.784 | - | |
Proposed | 0.983 | 0.973 | 0.992 | 0.982 | 0.965 | 0.966 | 0.992 | |
ARIA | Kar et al. [41] | 0.963 | 0.718 | 0.984 | - | - | - | 0.795 |
Vostatek et al. [42] | 0.94 | - | - | - | - | - | - | |
Prajna and Nath [43] | 0.925 | 0.566 | 0.961 | 0.649 | 0.48 | - | - | |
Proposed | 0.971 | 0.974 | 0.969 | 0.97 | 0.942 | 0.943 | 0.966 |
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Sebastian, A.; Elharrouss, O.; Al-Maadeed, S.; Almaadeed, N. GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation. Bioengineering 2024, 11, 4. https://doi.org/10.3390/bioengineering11010004
Sebastian A, Elharrouss O, Al-Maadeed S, Almaadeed N. GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation. Bioengineering. 2024; 11(1):4. https://doi.org/10.3390/bioengineering11010004
Chicago/Turabian StyleSebastian, Anila, Omar Elharrouss, Somaya Al-Maadeed, and Noor Almaadeed. 2024. "GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation" Bioengineering 11, no. 1: 4. https://doi.org/10.3390/bioengineering11010004