On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images
Abstract
:1. Introduction
1.1. Ophthalmic Imaging
1.2. OCT Types
1.2.1. Time-Domain OCT (TD-OCT)
1.2.2. Fourier-Domain OCT (FD-OCT)
1.2.3. Swept-Source OCT
2. Clinical Interpretation of OCT Images with Different Biomarkers
3. Most Common Disease Identification Using OCT Image Analysis
3.1. Glaucoma
3.2. Age-Related Macular Degeneration (ARMD)
3.3. Macular Oedema
3.4. Diabetic Retinopathy
4. Analysis of Optical Coherence Tomography Images
4.1. Denoising OCT Images
4.2. Segmentation of Subretinal Layers of OCT Images
4.3. Detection of Various Pathologies in OCT Images
4.4. Deep Learning Approach for OCT Image Analysis
5. Publicly Available OCT Dataset
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Biomarkers | Association of the Biomarkers with the Eye Disease |
---|---|
Layer thickness | Changes in the retina’s thickness and its layers are characteristics of many diseases such as glaucoma and age-related macular degeneration (AMD). For example: a glaucoma patient has 20% lower RNFL (retinal nerve fibre layer) thickness than normal patient |
Inner retinal lesion | A characteristic finding in various stages of diabetic macular oedema (DME) that is a key risk factor for developing more advanced stages of DME. |
Drusen | A characteristic finding of the early stages of AMD that is a key risk factor for the development of more advanced stages. |
Cup–disc ratio | A cup–disc ratio of more than 0.5 is a risk sign of glaucoma. |
PVD | An early sign of macular 0edema and lamellar macular hole |
Pre-Processing Methods | Researchers | Key Point | Evaluation Parameters |
---|---|---|---|
Deep learning (Unet and SRResNet and AC-SRResNet) | Y. Huang et al. [37] | This is the modification of the existing combination of U-Net and Super-Resolution Residual network with the addition of asymmetric convolution. The evaluation parameters used in this paper were signal-to-noise ratio, contrast to noise ratio, and edge preservation index. | SNR (dB) U-Net: 19.36 SRResNet: 20.11 AC-SRResNet: 22.15 |
SiameseGAN | K. Nilesh et al. [33] | This is the combination of Siamese network module and a generative adversarial network. This model helps generate denoised images closer to the ground-truth image. | Mean PSNR: 28.25 dB |
Semi-Supervised (N2NSR-OCT) | Q. Bin et al. [31] | This paper utilises up- and down-sampling networks, consisting of modified U-net and DPBN, to obtain a super-resolution image. | PSNR: 20.7491 dB RMSE: 0.0956 dB MS: SSIM 0.8205 |
Semi-Supervised Capsule cGAN | M. Wang et al. [32] | This paper addresses the issue of speckle noise with a semi-supervised learning-based algorithm. A capsule cognitive generative adversarial network is used to construct the learning system, and the structural information loss is regained by using a semi-supervised loss function. | SNR: 59.01 dB |
None | Yazdanpanah A. [38], Abramoff M.D. [39], Yang Q. [40] S. Bekalo et al. [41] | These researchers did not use any pre-processing algorithm for the oct image analysis. | DSC: 0.85 Correlation C/D: 0.93 |
2D linear smoothing | Huang Y. et al. [42] | This paper uses 3 × 3 pixel boxcar averaging filter to reduce speckle noise. | Avg. mean: 0.51 SD: 0.49 |
Mean filter | J. Xu et al. [14] | In this paper, the author investigated the possibility of variable-size super pixel analysis for early detection of glaucoma. The ncut algorithm is used to map variable-size superpixels on a 2D feature map by grouping similar neighbouring pixels. | AUC: 0.855 |
Wavelet shrinkage | Quellec G. et al. [43] | This paper describes an automated method for detection of the footprint of symptomatic exudate-associated derangements (SEADs) in SD-OCT scans from AMD patients. An adaptive wavelet transformation is used to extract a 3D textural feature. | AUC: 0.961 |
Adaptive vector-valued kernel function | Mishra A. et al. [44] | This paper proposes a two-step kernel-based optimisation scheme to identify the location of layers and then refine them to obtain their segmentation. | NA |
Two 1D filters: (1) median filtering along the A-scans; (2) Gaussian kernel in the longitudinal direction | Baroni M. et al. [45] | The layer identification is made by smoothing the OCT image with a median filter on the A-scan and a Gaussian field on the B-scan image. | Correlation: 5.13 Entropy: 25.65 |
SVM approach | Fuller A.R. et al. [46] | This paper uses the SVM approach by considering a voxel’s mean value and variance with various resolution settings to handle the noise and decrease the classification error. | SD: 6.043 |
Low-pass filtering | Hee M.R. et al. [5] | The peak position of the OCT image was filtered out using a low-pass filter to create similarity in spatial frequency distribution in the axial position. | NA |
Reference | Fluid Type | Disease | Year | Method | Evaluation Parameter |
---|---|---|---|---|---|
Gopinath et al. [51] | IRF | AMD, RVO, DME | 2019 | Selective enhancement of cyst using generalised motion pattern (GMP) and CNN | Mean DC: 0.71 |
Y. Derradji et al. [52] | Retinal atrophy | AMD | 2021 | CNN and Residual U-shaped Network | Mean Dice score: 0.881 Sensitivity: 0.85 Precision: 0.92 |
Y. Guo et al. [53] | IRF, SRF, PED | DME | 2020 | ReF-Net | F1 score: 0.892 |
Marc Wilson et al. [54] | IRF, PED | AMD | 2021 | Various DL Models | DSC: 0.43–0.78 |
B. Sappa et al. [41] | IRF, SRF, PED | AMD | 2021 | RetFluidNet (based on auto-encoder) | Accuracy IRF: 80.05% PED: 92.74% PED: 95.53% |
Girish et al. [55] | IRF | AMD, RVO, DME | 2019 | Fully connected neural network (FCNN) | Dice rate: 0.71 |
Venhuizen et al. [56] | IRF | AMD, RVO, DME | 2018 | Cascade of neural networks to form DL algorithm | DC: 0.75 |
Schlegl et al. [57] | IRF, SRF | AMD, RVO, DME | 2018 | Auto-encoder | AUC: 0.94 |
Retouch [1] | IRF, SRF, PED | AMD, RVO | 2019 | Various models proposed by participants | DSC: 0.7–0.8 |
Xu et al. [50] | Any | AMD | 2015 | Stratified sampling voxel classification for feature extraction and graph method for layer segmentation | TPR: 96% TNR: 0.16% |
Chiu et al. [58] | Any | DME | 2015 | Kernel regression method to estimate fluid and graph theory and dynamic programming (GTDP) for boundary segmentation | DC: 0.78 |
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Karn, P.K.; Abdulla, W.H. On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images. Bioengineering 2023, 10, 407. https://doi.org/10.3390/bioengineering10040407
Karn PK, Abdulla WH. On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images. Bioengineering. 2023; 10(4):407. https://doi.org/10.3390/bioengineering10040407
Chicago/Turabian StyleKarn, Prakash Kumar, and Waleed H. Abdulla. 2023. "On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images" Bioengineering 10, no. 4: 407. https://doi.org/10.3390/bioengineering10040407