Clinical Perspectives on Retinal Image Processing Models: A Comprehensive Statistical Review

Authors

  • Jagadale Sachin Mohan PhD Scholar Oriental University, Indore. M.P., India
  • L. K. Vishwamitra Dept. of CSE Oriental University, Indore M.P., India

Keywords:

Retinal Image Processing, Machine Learning Models, Diagnostic Accuracy, Ophthalmic Care

Abstract

The field of retinal image processing is pivotal for early detection and treatment of retinal diseases, major contributors to global vision impairment. Despite rapid advancements, current machine learning models in these domain exhibit significant limitations, spanning pre-processing, segmentation, classification methodologies, and post-processing inconsistencies. This paper care­fully examines many math models by comparing how the­y work and how well they do their job. It use­s a good way to look at data closely. This finds what models are good at and not good at. This he­lps with making models better in the­ future for looking at eye picture­s. The review shows what is diffe­rent betwee­n how the models work. It gives ide­as to fix problems in old models and make ne­wer models more corre­ct and helpful for doctors. It is important because the­ review shows how the mode­ls can help doctors be more right about diagnose­s, how sick people are, and tre­atments for eye proble­ms. By learning from what is known now, this work adds to what others have le­arned. It also sets up work for bette­r and smarter models for looking at eye­ pictures later on. This rese­arch is a key step to bette­r outcomes for patients and improveme­nts in eye doctor care. It shows that work must ke­ep happening to make re­tinal image models bette­r. This will help make bette­r ways for doctors to diagnose and treat eye­ problems.

Downloads

Download data is not yet available.

References

J. Wei et al., "Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm," in IEEE Transactions on Medical Imaging, vol. 41, no. 2, pp. 292-307, Feb. 2022, doi: 10.1109/TMI.2021.3111679.

[M. Rahil, B. N. Anoop, G. N. Girish, A. R. Kothari, S. G. Koolagudi and J. Rajan, "A Deep Ensemble Learning-Based CNN Architecture for Multiclass Retinal Fluid Segmentation in OCT Images," in IEEE Access, vol. 11, pp. 17241-17251, 2023, doi: 10.1109/ACCESS.2023.3244922.

C. An, Y. Wang, J. Zhang and T. Q. Nguyen, "Self-Supervised Rigid Registration for Multimodal Retinal Images," in IEEE Transactions on Image Processing, vol. 31, pp. 5733-5747, 2022, doi: 10.1109/TIP.2022.3201476.

Y. Li et al., "Global Transformer and Dual Local Attention Network via Deep-Shallow Hierarchical Feature Fusion for Retinal Vessel Segmentation," in IEEE Transactions on Cybernetics, vol. 53, no. 9, pp. 5826-5839, Sept. 2023, doi: 10.1109/TCYB.2022.3194099.

Ajani, S. N. ., Khobragade, P. ., Dhone, M. ., Ganguly, B. ., Shelke, N. ., & Parati, N. . (2023). Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 546–559

J. Zhang et al., "Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks," in IEEE Transactions on Image Processing, vol. 31, pp. 823-838, 2022, doi: 10.1109/TIP.2021.3135708.

J. Lyu, Y. Zhang, Y. Huang, L. Lin, P. Cheng and X. Tang, "AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation," in IEEE Transactions on Medical Imaging, vol. 41, no. 12, pp. 3699-3711, Dec. 2022, doi: 10.1109/TMI.2022.3193146.

N. Saeedizadeh et al., "A Device-Independent, Shape Preserving Retinal Optical Coherence Tomography Image Alignment Method Applying TV-Unet for RPE Layer Detection," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-8, 2022, Art no. 4504408, doi: 10.1109/TIM.2022.3185653.

K. Zhou et al., "Proxy-Bridged Image Reconstruction Network for Anomaly Detection in Medical Images," in IEEE Transactions on Medical Imaging, vol. 41, no. 3, pp. 582-594, March 2022, doi: 10.1109/TMI.2021.3118223.

Y. Li, Y. Zhang, W. Cui, B. Lei, X. Kuang and T. Zhang, "Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation," in IEEE Transactions on Medical Imaging, vol. 41, no. 8, pp. 1975-1989, Aug. 2022, doi: 10.1109/TMI.2022.3151666.

C. Yao et al., "Joint Segmentation of Multi-Class Hyper-Reflective Foci in Retinal Optical Coherence Tomography Images," in IEEE Transactions on Biomedical Engineering, vol. 69, no. 4, pp. 1349-1358, April 2022, doi: 10.1109/TBME.2021.3115552.

L. Ding, T. D. Kang, A. E. Kuriyan, R. S. Ramchandran, C. C. Wykoff and G. Sharma, "Combining Feature Correspondence With Parametric Chamfer Alignment: Hybrid Two-Stage Registration for Ultra-Widefield Retinal Images," in IEEE Transactions on Biomedical Engineering, vol. 70, no. 2, pp. 523-532, Feb. 2023, doi: 10.1109/TBME.2022.3196458.

J. Hao et al., "Retinal Structure Detection in OCTA Image via Voting-Based Multitask Learning," in IEEE Transactions on Medical Imaging, vol. 41, no. 12, pp. 3969-3980, Dec. 2022, doi: 10.1109/TMI.2022.3202183.

N. Liang, L. Yuan, X. Wen, H. Xu and J. Wang, "End-To-End Retina Image Synthesis Based on CGAN Using Class Feature Loss and Improved Retinal Detail Loss," in IEEE Access, vol. 10, pp. 83125-83137, 2022, doi: 10.1109/ACCESS.2022.3196377.

Y. Tan, K. -F. Yang, S. -X. Zhao and Y. -J. Li, "Retinal Vessel Segmentation With Skeletal Prior and Contrastive Loss," in IEEE Transactions on Medical Imaging, vol. 41, no. 9, pp. 2238-2251, Sept. 2022, doi: 10.1109/TMI.2022.3161681.

J. Hu et al., "Multi-Scale Interactive Network With Artery/Vein Discriminator for Retinal Vessel Classification," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 8, pp. 3896-3905, Aug. 2022, doi: 10.1109/JBHI.2022.3165867.

A. Desiani, Erwin, B. Suprihatin, F. Efriliyanti, M. Arhami and E. Setyaningsih, "VG-DropDNet a Robust Architecture for Blood Vessels Segmentation on Retinal Image," in IEEE Access, vol. 10, pp. 92067-92083, 2022, doi: 10.1109/ACCESS.2022.3202890.

M. Wang et al., "MsTGANet: Automatic Drusen Segmentation From Retinal OCT Images," in IEEE Transactions on Medical Imaging, vol. 41, no. 2, pp. 394-406, Feb. 2022, doi: 10.1109/TMI.2021.3112716.

Q. Meng, L. Liao and S. Satoh, "Weakly-Supervised Learning With Complementary Heatmap for Retinal Disease Detection," in IEEE Transactions on Medical Imaging, vol. 41, no. 8, pp. 2067-2078, Aug. 2022, doi: 10.1109/TMI.2022.3155154.

R. Rasti, A. Biglari, M. Rezapourian, Z. Yang and S. Farsiu, "RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation," in IEEE Transactions on Medical Imaging, vol. 42, no. 5, pp. 1413-1423, May 2023, doi: 10.1109/TMI.2022.3228285.

Y. Xie et al., "Fundus Image-Label Pairs Synthesis and Retinopathy Screening via GANs With Class-Imbalanced Semi-Supervised Learning," in IEEE Transactions on Medical Imaging, vol. 42, no. 9, pp. 2714-2725, Sept. 2023, doi: 10.1109/TMI.2023.3263216.

Y. Ye, C. Pan, Y. Wu, S. Wang and Y. Xia, "MFI-Net: Multiscale Feature Interaction Network for Retinal Vessel Segmentation," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 9, pp. 4551-4562, Sept. 2022, doi: 10.1109/JBHI.2022.3182471.

J. Yang et al., "Self-Supervised Sequence Recovery for Semi-Supervised Retinal Layer Segmentation," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 8, pp. 3872-3883, Aug. 2022, doi: 10.1109/JBHI.2022.3166778.

Y. Yuan, L. Zhang, L. Wang and H. Huang, "Multi-Level Attention Network for Retinal Vessel Segmentation," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 1, pp. 312-323, Jan. 2022, doi: 10.1109/JBHI.2021.3089201.

E. Guo, H. Fu, L. Zhou and D. Xu, "Bridging Synthetic and Real Images: A Transferable and Multiple Consistency Aided Fundus Image Enhancement Framework," in IEEE Transactions on Medical Imaging, vol. 42, no. 8, pp. 2189-2199, Aug. 2023, doi: 10.1109/TMI.2023.3247783.

K. Hu, S. Jiang, Y. Zhang, X. Li and X. Gao, "Joint-Seg: Treat Foveal Avascular Zone and Retinal Vessel Segmentation in OCTA Images as a Joint Task," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-13, 2022, Art no. 4007113, doi: 10.1109/TIM.2022.3193188.

S. H. Abbood, H. N. A. Hamed, M. S. M. Rahim, A. Rehman, T. Saba and S. A. Bahaj, "Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model," in IEEE Access, vol. 10, pp. 73079-73086, 2022, doi: 10.1109/ACCESS.2022.3189374.

V. M. R. Sankari, U. Umapathy, S. Alasmari and S. M. Aslam, "Automated Detection of Retinopathy of Prematurity Using Quantum Machine Learning and Deep Learning Techniques," in IEEE Access, vol. 11, pp. 94306-94321, 2023, doi: 10.1109/ACCESS.2023.3311346.

L. Pan and X. Chen, "Retinal OCT Image Registration: Methods and Applications," in IEEE Reviews in Biomedical Engineering, vol. 16, pp. 307-318, 2023, doi: 10.1109/RBME.2021.3110958.

Z. Qu, L. Zhuo, J. Cao, X. Li, H. Yin and Z. Wang, "TP-Net: Two-Path Network for Retinal Vessel Segmentation," in IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 4, pp. 1979-1990, April 2023, doi: 10.1109/JBHI.2023.3237704.

M. Monemian and H. Rabbani, "A Computationally Efficient Red-Lesion Extraction Method for Retinal Fundus Images," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13, 2023, Art no. 5001613, doi: 10.1109/TIM.2022.3229712.

N. Paluru, H. Ravishankar, S. Hegde and P. K. Yalavarthy, "Self Distillation for Improving the Generalizability of Retinal Disease Diagnosis Using Optical Coherence Tomography Images," in IEEE Journal of Selected Topics in Quantum Electronics, vol. 29, no. 4: Biophotonics, pp. 1-12, July-Aug. 2023, Art no. 7200812, doi: 10.1109/JSTQE.2023.3240729.

M. A. Rodríguez, H. AlMarzouqi and P. Liatsis, "Multi-Label Retinal Disease Classification Using Transformers," in IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 6, pp. 2739-2750, June 2023, doi: 10.1109/JBHI.2022.3214086.

S. Mishra, Y. Zhang, D. Z. Chen and X. S. Hu, "Data-Driven Deep Supervision for Medical Image Segmentation," in IEEE Transactions on Medical Imaging, vol. 41, no. 6, pp. 1560-1574, June 2022, doi: 10.1109/TMI.2022.3143371.

M. Tajmirriahi, R. Kafieh, Z. Amini and V. Lakshminarayanan, "A Dual-Discriminator Fourier Acquisitive GAN for Generating Retinal Optical Coherence Tomography Images," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-8, 2022, Art no. 5015708, doi: 10.1109/TIM.2022.3189735.

M. Wang et al., "Self-Guided Optimization Semi-Supervised Method for Joint Segmentation of Macular Hole and Cystoid Macular Edema in Retinal OCT Images," in IEEE Transactions on Biomedical Engineering, vol. 70, no. 7, pp. 2013-2024, July 2023, doi: 10.1109/TBME.2023.3234031.

T. Shi et al., "Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation," in IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 8, pp. 4006-4017, Aug. 2023, doi: 10.1109/JBHI.2023.3274789.

R. Chaurasia and A. Sengupta, "Retinal Biometric for Securing JPEG-Codec Hardware IP Core for CE Systems," in IEEE Transactions on Consumer Electronics, vol. 69, no. 3, pp. 441-457, Aug. 2023, doi: 10.1109/TCE.2023.3264669.

K. Aurangzeb, R. S. Alharthi, S. I. Haider and M. Alhussein, "An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation," in IEEE Access, vol. 11, pp. 23107-23118, 2023, doi: 10.1109/ACCESS.2022.3217782.

W. Liu et al., "Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 9, pp. 4623-4634, Sept. 2022, doi: 10.1109/JBHI.2022.3188710.

S. Schürer-Waldheim, P. Seeböck, H. Bogunović, B. S. Gerendas and U. Schmidt-Erfurth, "Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 8, pp. 3927-3937, Aug. 2022, doi: 10.1109/JBHI.2022.3166068.

X. He, Z. Zhong, L. Fang, M. He and N. Sebe, "Structure-Guided Cross-Attention Network for Cross-Domain OCT Fluid Segmentation," in IEEE Transactions on Image Processing, vol. 32, pp. 309-320, 2023, doi: 10.1109/TIP.2022.3228163.

B. Goutam, M. F. Hashmi, Z. W. Geem and N. D. Bokde, "A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images," in IEEE Access, vol. 10, pp. 57796-57823, 2022, doi: 10.1109/ACCESS.2022.3178372.

S. Deari, I. Oksuz and S. Ulukaya, "Block Attention and Switchable Normalization Based Deep Learning Framework for Segmentation of Retinal Vessels," in IEEE Access, vol. 11, pp. 38263-38274, 2023, doi: 10.1109/ACCESS.2023.3265729.

[Y. Zhang, M. Liu, F. Yu, T. Zeng and Y. Wang, "An O-Shape Neural Network With Attention Modules to Detect Junctions in Biomedical Images Without Segmentation," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 2, pp. 774-785, Feb. 2022, doi: 10.1109/JBHI.2021.3094187.

H. Hao et al., "Sparse-Based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 9, pp. 4402-4413, Sept. 2022, doi: 10.1109/JBHI.2022.3194025.

Z. Zhang, Z. Ji, Q. Chen, S. Yuan and W. Fan, "Joint Optimization of CycleGAN and CNN Classifier for Detection and Localization of Retinal Pathologies on Color Fundus Photographs," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 1, pp. 115-126, Jan. 2022, doi: 10.1109/JBHI.2021.3092339.

Y. Niu, L. Gu, Y. Zhao and F. Lu, "Explainable Diabetic Retinopathy Detection and Retinal Image Generation," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 1, pp. 44-55, Jan. 2022, doi: 10.1109/JBHI.2021.3110593.

F. Chen et al., "Dual-Path and Multi-Scale Enhanced Attention Network for Retinal Diseases Classification Using Ultra-Wide-Field Images," in IEEE Access, vol. 11, pp. 45405-45415, 2023, doi: 10.1109/ACCESS.2023.3273613.

X. Zhang et al., "T-Net: Hierarchical Pyramid Network for Microaneurysm Detection in Retinal Fundus Image," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13, 2023, Art no. 5019613, doi: 10.1109/TIM.2023.3286003.

J. Li, G. Gao, L. Yang, G. Bian and Y. Liu, "DPF-Net: A Dual-Path Progressive Fusion Network for Retinal Vessel Segmentation," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-17, 2023, Art no. 2517817, doi: 10.1109/TIM.2023.3277946.

Downloads

Published

07.01.2024

How to Cite

Mohan , J. S. ., & Vishwamitra, L. K. . (2024). Clinical Perspectives on Retinal Image Processing Models: A Comprehensive Statistical Review. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 295–309. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4378

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.