RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs Using a Novel Multi-scale Generative Adversarial Network

Kamran, Sharif Amit; Hossain, Khondker Fariha; Tavakkoli, Alireza; Zuckerbrod, Stewart Lee; Sanders, Kenton M.; Baker, Salah A.

doi:10.1007/978-3-030-87237-3_4

Cited by 67 publications

(45 citation statements)

References 29 publications

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“…Some authors also proposed a scalable self-supervised learning from the pretrained large ViT models, which could also be adopted into semi-supervised GANs architecture to further improve the performance 46 . Further studies will involve using other transformer or GAN models such as retinal vascular GAN or vision transformer GAN 47,48 …”

Section: Discussionmentioning

confidence: 99%

Development and Clinical Validation of Semi-Supervised Generative Adversarial Networks for Detection of Retinal Disorders in Optical Coherence Tomography Images Using Small Dataset

Zheng

Yang

et al. 2022

Asia-Pacific Journal of Ophthalmology

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Purpose: To develop and test semi-supervised generative adversarial networks (GANs) that detect retinal disorders on optical coherence tomography (OCT) images using a small-labeled dataset. Methods: From a public database, we randomly chose a small supervised dataset with 400 OCT images (100 choroidal neovascularization, 100 diabetic macular edema, 100 drusen, and 100 normal) and assigned all other OCT images to unsupervised dataset (107,912 images without labeling). We adopted a semi-supervised GAN and a supervised deep learning (DL) model for automatically detecting retinal disorders from OCT images. The performance of the 2 models was compared in 3 testing datasets with different OCT devices. The evaluation metrics included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves. Results: The local validation dataset included 1000 images with 250 from each category. The independent clinical dataset included 366 OCT images using Cirrus OCT Shanghai Shibei Hospital and 511 OCT images using RTVue OCT from Xinhua Hospital respectively. The semi-supervised GANs classifier achieved better accuracy than supervised DL model (0.91 vs 0.86 for local cell validation dataset, 0.91 vs 0.86 in the Shanghai Shibei Hospital testing dataset, and 0.93 vs 0.92 in Xinhua Hospital testing dataset). For detecting urgent referrals (choroidal neovascularization and diabetic macular edema) from nonurgent referrals (drusen and normal) on OCT images, the semi-supervised GANs classifier also achieved better area under the receiver operating characteristic curves than supervised DL model (0.99 vs 0.97, 0.97 vs 0.96, and 0.99 vs 0.99, respectively). Conclusions: A semi-supervised GAN can achieve better performance than that of a supervised DL model when the labeled dataset is limited. The current study offers utility to various research and clinical studies using DL with relatively small datasets. Semi-supervised GANs can detect retinal disorders from OCT images using relatively small dataset.

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Section: Discussionmentioning

confidence: 99%

Development and Clinical Validation of Semi-Supervised Generative Adversarial Networks for Detection of Retinal Disorders in Optical Coherence Tomography Images Using Small Dataset

Zheng

Yang

et al. 2022

Asia-Pacific Journal of Ophthalmology

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“…The approach is powerful, but limited to mimicking the quality and nature of the input images. As such, it is unsuitable for our research, though promising as a foundation for segmentation networks 21,34 . Transforming the original data is most often used for robustness tests 12,35‐40 in a non‐adversarial way.…”

Section: Related Workmentioning

confidence: 99%

“…There are also other UNet based architectures used for retinal image segmentation which are not tested in this article, but also provide state of the art performance 23,54 . GANs are among top performers in retinal vessel segmentation task 34 . GANs are composed of discriminator and generator networks—one to create the segmentation output, and another to evaluate its quality.…”

Section: Related Workmentioning

confidence: 99%

“…As such, it is unsuitable for our research, though promising as a foundation for segmentation networks. 21,34 Transforming the original data is most often used for robustness tests 12,[35][36][37][38][39][40] in a non-adversarial way. This is the approach we also follow in our research.…”

Section: Related Workmentioning

confidence: 99%

“…23,54 GANs are among top performers in retinal vessel segmentation task. 34 GANs are composed of discriminator and generator networks-one to create the segmentation output, and another to evaluate its quality. GANs used in this field of application typically have a UNet based discriminator.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Robustness of deep learning methods for ocular fundus segmentation: Evaluation of blur sensitivity

Petrović

Gojić

Dragan

et al. 2022

Concurrency and Computation

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This article analyzes the sensitivity of deep learning methods for ocular fundus segmentation. We use an empirical methodology based on non-adversarial perturbed datasets. The research is motivated by mass screening and self-administered tests in which AI methods are needed and may be given images with focus issues. These substandard pictures are simulated using blurring algorithms of varying designs and kernel sizes which are subjected to a test of inter-network and inter-dataset sensitivity using Gaussian noise as a control. We test for simulated defocusing, motion blur, and Gaussian noise. The DRIVE, CHASE, and STARE datasets were used to generate 441 synthetic datasets for testing. Analysis of the resultant n = 37,888 sample has identified anomalies illuminating failure modes of state-of-the-art ocular segmentation models. Data analysis led us to conclude that accuracy is a poor measure of sensitivity to input change and that even moderate levels of blur have dramatic effects on said sensitivity. We further concluded that architectures show larger variations in sensitivity to blur which do not match either their reported or measured performance on unblurred test datasets. Our analysis has attributed at least a part of the problem to overfitting non-essential input dataset features and resolution sensitivity.

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Improving sensitivity and connectivity of retinal vessel segmentation via error discrimination network

et al. 2022

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Purpose Automated retinal vessel segmentation is crucial to the early diagnosis and treatment of ophthalmological diseases. Many deep‐learning‐based methods have shown exceptional success in this task. However, current approaches are still inadequate in challenging vessels (e.g., thin vessels) and rarely focus on the connectivity of vessel segmentation. Methods We propose using an error discrimination network (D) to distinguish whether the vessel pixel predictions of the segmentation network (S) are correct, and S is trained to obtain fewer error predictions of D. Our method is similar to, but not the same as, the generative adversarial network. Three types of vessel samples and corresponding error masks are used to train D, as follows: (1) vessel ground truth; (2) vessel segmented by S; (3) artificial thin vessel error samples that further improve the sensitivity of D to wrong small vessels. As an auxiliary loss function of S, D strengthens the supervision of difficult vessels. Optionally, we can use the errors predicted by D to correct the segmentation result of S. Results Compared with state‐of‐the‐art methods, our method achieves the highest scores in sensitivity (86.19%, 86.26%, and 86.53%) and G‐Mean (91.94%, 91.30%, and 92.76%) on three public datasets, namely, STARE, DRIVE, and HRF. Our method also maintains a competitive level in other metrics. On the STARE dataset, the F1‐score and area under the receiver operating characteristic curve (AUC) of our method rank second and first, respectively, reaching 84.51% and 98.97%. The top scores of the three topology‐relevant metrics (Conn, Inf, and Cor) demonstrate that the vessels extracted by our method have excellent connectivity. We also validate the effectiveness of error discrimination supervision and artificial error sample training through ablation experiments. Conclusions The proposed method provides an accurate and robust solution for difficult vessel segmentation.

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RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs Using a Novel Multi-scale Generative Adversarial Network

Cited by 67 publications

References 29 publications

Development and Clinical Validation of Semi-Supervised Generative Adversarial Networks for Detection of Retinal Disorders in Optical Coherence Tomography Images Using Small Dataset

Development and Clinical Validation of Semi-Supervised Generative Adversarial Networks for Detection of Retinal Disorders in Optical Coherence Tomography Images Using Small Dataset

Robustness of deep learning methods for ocular fundus segmentation: Evaluation of blur sensitivity

Improving sensitivity and connectivity of retinal vessel segmentation via error discrimination network

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