Ophthalmic Image Generation using GAN for Branch Retinal Vein Occlusions and Laser Spots
Keywords:
Adversarial networks, Branch Retinal Vein Occlusions, Laser Spots, GANs, Generative Adversarial Networks, Ophthalmic imagesAbstract
This research paper presents a novel methodology for generating ophthalmic images depicting Laser Spots and Branch Retinal Vein Occlusions via the application of Generative Adversarial Networks (GANs). The study's dataset includes images of both Laser Spots and Branch Retinal Vein Occlusions. Prior to GAN model training, the images undergo essential preprocessing steps, involving uniform resizing, and pixel value standardization. The GAN architecture is designed with a generator network and a discriminator network, operating collaboratively to yield images similar to the input samples and appraise their quality, respectively. After preparing the dataset of ophthalmic images, the generator and discriminator network were employed to construct the GAN model. After the model is trained, the generator network is effectively employed to synthesize new ophthalmic images of Laser Spots and Branch Retinal Vein Occlusions. The generated images were evaluated using various metrics such as visual inspection, quantitative analysis, and comparison with existing images. The experimental outcomes demonstrate that the GAN model successfully generates superior-quality ophthalmic images of Laser Spots and Branch Retinal Vein Occlusions, closely resembling the attributes of the input images. The evaluation of Fréchet Inception Distance (FID) scores demonstrates the promising quality of GAN-generated images. The generated images can potentially be used for diagnostic, educational, and research purposes.
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R. Kumar and R. Malik, "A review on generative adversarial networks used for image reconstruction in medical imaging," Proceedings of the 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 03-04 September 2021, pp. 1-5.
C. Han, H. Hayashi, L. Rundo, R. Araki, W. Shimoda, S. Muramatsu, Y. Furukawa, G. Mauri, H. Nakayama, "GAN-based synthetic brain MR image generation," Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 04-07 April 2018, pp. 734-738.
O. Bernabé, E. Acevedo, A. Acevedo, R. Carreño, S. Gómez, "Classification of eye diseases in fundus images," IEEE Access 9 (2021): 101267-101276.
Tahir, Faraz, M. Usman Akram, Mujahid Abbass, and Albab Ahmad Khan. "Laser marks detection from fundus images." In 2014 14th International Conference on Hybrid Intelligent Systems, pp. 147-151. IEEE, 2014.
N. Rajapaksha, L. Ranathunga, K. M. P. K. Bandara, "Detection of Central Retinal Vein Occlusion using Guided Salient Features," Proceedings of the 2019 Digital Image Computing: Techniques and Applications (DICTA), 02-04 December 2019, pp. 1-6.
X. Mao, Q. Li, "Generative Adversarial Networks for Image Generation," Springer Singapore, 2021.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, A. A. Bharath, "Generative Adversarial Networks: An Overview," IEEE Signal Processing Magazine, Volume: 35, Issue: 1, pp. 53-65, IEEE, 2018.
S. Kumar, S. Dhawan, "A detailed study on generative adversarial networks," Proceedings of the 5th International Conference on Communication and Electronics Systems (ICCES), 10-12 June 2020, pp. 641-645.
Y. Cao, L. Jia, Y. Chen, N. Lin, C. Yang, B. Zhang, Z. Liu, X. Li, H. Dai, "Recent Advances of Generative Adversarial Networks in Computer Vision," IEEE Access, Volume: 7, 2018, pp.14985-15006.
C. Li, Y. Su, W. Liu, "Text-to-text generative adversarial networks," Proceedings of the International Joint Conference on Neural Networks (IJCNN), 08-13 July 2018, pp. 1-7.
Y. Liang, H. Yao, "Research on GAN-based container code images generation method," Proceedings of the 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 16-19 October 2020, pp. 198-201.
Z. Zhang, "Research Progress on Generative Adversarial Network with its Applications," Proceedings of the 5th Information Technology and Mechatronics Engineering Conference (ITOEC), 12-14 June 2020, pp. 396-399.
T. Iqbal, H. Ali, "Generative adversarial network for medical images (MI-GAN)," Journal of medical systems, Volume: 42, Springer, 2018, pp. 1-11.
W. Ahmad, H. Ali, Z. Shah, S. Azmat, "A new generative adversarial network for medical images super resolution," Scientific Reports, Volume: 12, Article No. 9533, Springer Nature, 2022.
D. Mukherkjee, P. Saha, D. Kaplun, A. Sinitca, R. Sarkar, "Brain tumor image generation using an aggregation of GAN models with style transfer." Scientific Reports, Volume: 12, Article No. 9141, Springer Nature, 2022, pp. 1-16.
C. Han, L. Rundo, R. Araki, Y. Nagano, Y. Furukawa, G. Mauri, H. Nakayama, H. Hayashi, "Combining noise-to-image and image-to-image GANs: Brain MR image augmentation for tumor detection," IEEE Access, Volume: 7, 16 October 2019, pp. 156966 – 156977.
S. Motamed, P. Rogalla, F. Khalvati, "Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images," Informatics in Medicine Unlocked, Volume: 27, 2021, 100779.
H. J. Kong, J. Y. Kim, H. M. Moon, H. C. Park, J. W. Kim, R. Lim, J. Woo, G. E. Fakhri, D. W. Kim, S. Kim, "Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging," Scientific Reports, Volume: 12, Article No. 18118, Springer Nature, 2022.
A. You, J. Kim, I. Ryu, T. Yoo, "Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey," Eye and Vision, Volume: 9, Article No. 6, Springer Nature, 2022.
V. Das, S. Dandapat, P. K. Bora, "Unsupervised super-resolution of OCT images using generative adversarial network for improved age-related macular degeneration diagnosis," IEEE Sensors Journal, Volume: 20, Issue: 15, 01 August 2020, IEEE, pp. 8746 – 8756.
Y. Zhou, B. Wang, X. He, S. Cui, L. Shao, "DR-GAN: conditional generative adversarial network for fine-grained lesion synthesis on diabetic retinopathy images," IEEE Journal of Biomedical and Health Informatics, Volume: 26, Issue: 1, January 2022, IEEE, pp. 56-66.
Y. Luo, K. Chen, L. Liu, J. Liu, J. Mao, G. Ke, M. Sun, "Dehaze of cataractous retinal images using an unpaired generative adversarial network," IEEE Journal of Biomedical and Health Informatics Volume: 24, Issue: 12, December 2020, pp. 3374 – 3383.
W. Fuhl, D. Geisler, W. Rosenstiel, E. Kasneci, "The applicability of Cycle GANs for pupil and eyelid segmentation, data generation and image refinement," Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
T. K. Yoo, J. Y. Choi, H. K. Kim, "A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease," Computers in Biology and Medicine, Volume: 118, March 2020.
A. Diaz-Pinto, A. Colomer, V. Naranjo, S. Morales, Y. Xu, A. F. Frangi, "Retinal image synthesis and semi-supervised learning for glaucoma assessment," IEEE Transactions on Medical Imaging, Volume: 38, Issue: 9, September 2019, pp. 2211 - 2218.
J. Chen, A. Coyner, R. V. Paul, M. Elizabeth Hartnett, D. Moshfeghi, L. Owen, J. Kalpathy-Cramer, M. Chiang, J. Campbell, "Deepfakes in ophthalmology: applications and realism of synthetic retinal images from generative adversarial networks," Ophthalmology Science, Volume: 1, Issue 4, December 2021.
T. Zhou, Q. Li, H. Lu, Q. Cheng, X. Zhang, "GAN review: Models and medical image fusion applications," Information Fusion, Volume: 91, 2023, Elsevier, pp. 134-148.
T. Dhar, N. Dey, S. Borra, R. S. Sherratt, "Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust," IEEE Transactions on Technology and Society, Volume: 4, no. 1, 2023, IEEE, pp. 68-75.
N. A. Mashudi, N. Ahmad, and N. M. Noor, "LiWGAN: A Light Method to Enhance Generative Adversarial Network Performance," IEEE Access, vol. 10, pp. 93155-93167, 2022.
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