A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation
Abstract
:1. Introduction
2. Related Work
2.1. Synthetic Image Generation
2.2. Image-to-Image Translation
2.3. Retinal Image Synthesis
2.4. Retinal Vessel Segmentation
3. Materials and Methods
3.1. The Benchmark Datasets
- DRIVE dataset—The DRIVE dataset [16] includes 40 retinal fundus images of size (20 images for training and 20 for test). The images were collected by a screening program for diabetic retinopathy in the Netherlands. Among the 40 photographs, 33 showed no diabetic retinopathy, while 7 showed mild early diabetic retinopathy. The segmentation ground-truth was provided both for the training and the test sets.
- CHASE_DB1 dataset—The CHASE_DB1 dataset [17] is composed by 28 fundus images of size , corresponding to the left and right eyes of 14 children. Each image is annotated by two independent human experts. An officially defined split between training and test is not provided for this dataset. In our experiments, we adopted the same strategy as [64,65], selecting the first 20 images for training and the remaining 8 for testing.
3.2. Vasculature Generation
3.3. Translating Vessel Maps into Retinal Images
3.4. The SMANet Architecture
3.5. Training Details
4. Results and Discussion
- SYNTH—the segmentation network was trained using only the 10,000 generated synthetic images;
- REAL—only real data were used to train the semantic segmentation network;
- SYNTH + REAL—synthetic data were used to pre-train the semantic segmentation network and real data were employed for fine-tuning.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Methods | AUC | Acc |
---|---|---|
SYNTH | 98.5% | 96.88% |
REAL | 98.48% | 96.87% |
SYNTH + REAL | 98.65% | 96.9% |
Methods | AUC | Acc |
---|---|---|
SYNTH | 98.64% | 97.49% |
REAL | 98.82% | 97.5% |
SYNTH + REAL | 99.16% | 97.72% |
Methods | AUC | Acc |
---|---|---|
SYNTH | 93.49% | 91.01% |
REAL | 98.48% | 96.87% |
SYNTH + REAL | 98.57% | 96.88% |
Methods | AUC | Acc |
---|---|---|
SYNTH | 66.96% | 92.62% |
REAL | 98.82% | 97.5% |
SYNTH + REAL | 98.87% | 97.65% |
Methods | AUC | Acc |
---|---|---|
One-Step (S) | 93.49% | 91.01% |
Two-Step (S) | 98.5% | 96.88% |
One-Step (S + R) | 98.57% | 96.88% |
Two-Step (S + R) | 98.65% | 96.90% |
Methods | AUC | Acc |
---|---|---|
One-Step (S) | 66.96% | 92.62% |
Two-Step (S) | 98.64% | 97.49% |
One-Step (S + R) | 98.87% | 97.65% |
Two-Step (S + R) | 99.16% | 97.72% |
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Andreini, P.; Ciano, G.; Bonechi, S.; Graziani, C.; Lachi, V.; Mecocci, A.; Sodi, A.; Scarselli, F.; Bianchini, M. A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation. Electronics 2022, 11, 60. https://doi.org/10.3390/electronics11010060
Andreini P, Ciano G, Bonechi S, Graziani C, Lachi V, Mecocci A, Sodi A, Scarselli F, Bianchini M. A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation. Electronics. 2022; 11(1):60. https://doi.org/10.3390/electronics11010060
Chicago/Turabian StyleAndreini, Paolo, Giorgio Ciano, Simone Bonechi, Caterina Graziani, Veronica Lachi, Alessandro Mecocci, Andrea Sodi, Franco Scarselli, and Monica Bianchini. 2022. "A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation" Electronics 11, no. 1: 60. https://doi.org/10.3390/electronics11010060
APA StyleAndreini, P., Ciano, G., Bonechi, S., Graziani, C., Lachi, V., Mecocci, A., Sodi, A., Scarselli, F., & Bianchini, M. (2022). A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation. Electronics, 11(1), 60. https://doi.org/10.3390/electronics11010060