Evaluation of Asymmetry in Right and Left Eyes of Normal Individuals Using Extracted Features from Optical Coherence Tomography and Fundus Images

Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image Registration

Background

Retinal imaging employs various modalities, each providing distinct perspectives on ocular structures. However, the integration of information from these modalities, which often have differing resolutions, requires effective image registration techniques. Existing retinal image registration methods typically rely on rigid or affine transformations, which may not adequately address the complexities of multimodal retinal images.

Method

This study introduces a nonrigid fuzzy image registration approach designed to align optical coherence tomography (OCT) images with fundus images. The method employs a fuzzy inference system (FIS) that uses vessel locations as key features for registration. The FIS applies specific rules to map points from the source image to the reference image, facilitating accurate alignment.

Results

The proposed method achieved a mean absolute registration error of 44.57 ± 39.38 µm in the superior-inferior orientation and 11.46 ± 10.06 µm in the nasal-temporal orientation. These results underscore the method's precision in aligning multimodal retinal images.

Conclusion

The nonrigid fuzzy image registration approach demonstrates robust and versatile performance in integrating multimodal retinal imaging data. Despite its straightforward implementation, the method effectively addresses the challenges of multimodal retinal image registration, providing a reliable tool for advanced ocular imaging analysis.

Design and Simulation of an Ultrahigh-resolution Spectral-domain Optical Coherence Tomography

Background

Optical coherence tomography (OCT) is a biomedical imaging technique used to achieve high-resolution images from human tissues in a noninvasive manner.

Methods

In this article, a practical approach is proposed for designing ultrahigh-resolution spectral-domain OCT (UHR SD-OCT) devices. At first, block diagram of a typical SD-OCT is introduced in detail. At second, internal components of each arm are introduced where the key parameters of each component are highlighted. At third, the effects of these key parameters on the overall performance of the UHR SD-OCT are investigated in a comprehensive manner. At fourth, the most important requirements of a UHR SD-OCT are explained, where suitable optical equipment is selected for each arm based on these requirements. At fifth, optical accessories as well as the electrical devices required for managing and control of the performance of a UHR SD-OCT are introduced in brief.

Results

Performance of the proposed device is assessed through various simulations, and finally, the implementation cost and implementation challenges are investigated in detail.

Conclusions

Simulation results indicate that the proposed UHR SD-OCT has acceptable axial resolution and imaging depth; hence, it is a good candidate for use in retinal applications that require UHR imaging.

Glaucoma detection: Binocular approach and clinical data in machine learning

In this work, we present a multi-modal machine learning method to automate early glaucoma diagnosis. The proposed methodology introduces two novel aspects for automated diagnosis not previously explored in the literature: simultaneous use of ocular fundus images from both eyes and integration with the patient's additional clinical data. We begin by establishing a baseline, termed monocular mode, which adheres to the traditional approach of considering the data from each eye as a separate instance. We then explore the binocular mode, investigating how combining information from both eyes of the same patient can enhance glaucoma diagnosis accuracy. This exploration employs the PAPILA dataset, comprising information from both eyes, clinical data, ocular fundus images, and expert segmentation of these images. Additionally, we compare two image-derived data modalities: direct ocular fundus images and morphological data from manual expert segmentation. Our method integrates Gradient-Boosted Decision Trees (GBDT) and Convolutional Neural Networks (CNN), specifically focusing on the MobileNet, VGG16, ResNet-50, and Inception models. SHAP values are used to interpret GBDT models, while the Deep Explainer method is applied in conjunction with SHAP to analyze the outputs of convolutional-based models. Our findings show the viability of considering both eyes, which improves the model performance. The binocular approach, incorporating information from morphological and clinical data yielded an AUC of 0.796 (±0.003 at a 95% confidence interval), while the CNN, using the same approach (both eyes), achieved an AUC of 0.764 (±0.005 at a 95% confidence interval).

Genetic susceptibility to schizophrenia through neuroinflammatory pathways is associated with retinal thinning: Findings from the UK-Biobank

The human retina is part of the central nervous system and can be easily and non-invasively imaged with optical coherence tomography. While imaging the retina may provide insights on central nervous system-related disorders such as schizophrenia, a typical challenge are confounders often present in schizophrenia which may negatively impact retinal health. Here, we therefore aimed to investigate retinal changes in the context of common genetic variations conveying a risk of schizophrenia as measured by polygenic risk scores. We used population data from the UK Biobank, including White British and Irish individuals without diagnosed schizophrenia, and estimated a polygenic risk score for schizophrenia based on the newest genome-wide association study (PGC release 2022). We hypothesized that greater genetic susceptibility to schizophrenia is associated with retinal thinning, especially within the macula. To gain additional mechanistic insights, we conducted pathway-specific polygenic risk score associations analyses, focusing on gene pathways that are related to schizophrenia. Of 65484 individuals recruited, 48208 participants with available matching imaging-genetic data were included in the analysis of whom 22427 (53.48%) were female and 25781 (46.52%) were male. Our robust principal component regression results showed that polygenic risk scores for schizophrenia were associated with retinal thinning while controlling for confounding factors (b = -0.03, p = 0.007, pFWER = 0.01). Similarly, we found that polygenic risk for schizophrenia specific to neuroinflammation gene sets revealed significant associations with retinal thinning (b = -0.03, self-contained p = 0.041 (reflecting the level of association), competitive p = 0.05 (reflecting the level of enrichment)). These results go beyond previous studies suggesting a relationship between manifested schizophrenia and retinal phenotypes. They indicate that the retina is a mirror reflecting the genetic complexities of schizophrenia and that alterations observed in the retina of individuals with schizophrenia may be connected to an inherent genetic predisposition to neurodegenerative aspects of the condition. These associations also suggest the potential involvement of the neuroinflammatory pathway, with indications of genetic overlap with specific retinal phenotypes. The findings further indicate that this gene pathway in individuals with a high polygenic risk for schizophrenia could contribute through acute-phase proteins to structural changes in the retina.

Wavelet scattering transform application in classification of retinal abnormalities using OCT images

To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a comprehensive comparison with the literature. The accuracies of classifying the OCT images of the OCTID dataset into two and five classes were [Formula: see text] and [Formula: see text], respectively. We achieved an accuracy of [Formula: see text] in detecting Diabetic Macular Edema from Normal ones using the TOPCON device-based dataset. Heidelberg and Duke datasets contain DME, Age-related Macular Degeneration, and Normal classes, in which we achieved accuracy of [Formula: see text] and [Formula: see text], respectively. A comparison of our results with the state-of-the-art models shows that our model outperforms these models for some assessments or achieves nearly the best results reported so far while having a much smaller computational complexity.

Decision Trees for Glaucoma Screening Based on the Asymmetry of the Retinal Nerve Fiber Layer in Optical Coherence Tomography

Purpose: The aim of this study was to analyze the relevance of asymmetry features between both eyes of the same patient for glaucoma screening using optical coherence tomography. Methods: Spectral-domain optical coherence tomography was used to estimate the thickness of the peripapillary retinal nerve fiber layer in both eyes of the patients in the study. These measurements were collected in a dataset from healthy and glaucoma patients. Several metrics for asymmetry in the retinal nerve fiber layer thickness between the two eyes were then proposed. These metrics were evaluated using the dataset by performing a statistical analysis to assess their significance as relevant features in the diagnosis of glaucoma. Finally, the usefulness of these asymmetry features was demonstrated by designing supervised machine learning models that can be used for the early diagnosis of glaucoma. Results: Machine learning models were designed and optimized, specifically decision trees, based on the values of proposed asymmetry metrics. The use of these models on the dataset provided good classification of the patients (accuracy 88%, sensitivity 70%, specificity 93% and precision 75%). Conclusions: The obtained machine learning models based on retinal nerve fiber layer asymmetry are simple but effective methods which offer a good trade-off in classification of patients and simplicity. The fast binary classification relies on a few asymmetry values of the retinal nerve fiber layer thickness, allowing their use in the daily clinical practice for glaucoma screening.

Interocular asymmetry of choroidal thickness and vascularity index measurements in normal eyes assessed by swept-source optical coherence tomography

BACKGROUND

To investigate the symmetry of interocular choroidal thickness and vascularity index measurements in normal eyes using swept-source optical coherence tomography (SS-OCT). Cross-sectional and observational study. This study included 244 eyes of 122 normal adults with ages uniformly distributed from 19 to 89 years.

METHODS

SS-OCT imaging was performed using a scanning pattern of 12×12 mm. Mean choroidal thickness (MCT) and choroidal vascularity index (CVI) measurements in the entire scanning region were obtained using a validated and published automatic method. The correlation and differences (including signed and absolute differences) between bilateral MCT and CVI measurements were analyzed at the following 6 regions: 3 concentric circles centered on the fovea with diameters of 2.5, 5, and 11 mm; the inner rim from 2.5 to 5 mm circle; the outer rim from 5 to 11 mm circle; and the entire 12×12-mm scan region, respectively. Comparison of interocular MCT and CVI measurements.

RESULTS

MCT measurements in right and left eyes were strongly correlated in all regions [all intraclass correlation (ICC) >0.73], but MCT measurements in right eyes were significantly thicker than in left eyes. CVI measurements in right and left eyes were moderately correlated in all regions (all ICC >0.46), but CVI measurements in right eyes were significantly smaller than that in left eyes in the macular subregions (2.5 mm circle, 5 mm circle, and the inner rim). Neither signed nor absolute interocular differences in MCT were correlated with corresponding CVI interocular differences.

CONCLUSIONS

Choroidal differences exist between normal fellow eyes in adults in the absence of obvious pathology. This study is useful in assisting clinicians and researchers in distinguishing asymmetric changes that are to be expected in normal eyes versus changes that could be associated with diseases.

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.

Synthetic OCT data in challenging conditions: three-dimensional OCT and presence of abnormalities

Nowadays, retinal optical coherence tomography (OCT) plays an important role in ophthalmology and automatic analysis of the OCT is of real importance: image denoising facilitates a better diagnosis and image segmentation and classification are undeniably critical in treatment evaluation. Synthetic OCT was recently considered to provide a benchmark for quantitative comparison of automatic algorithms and to be utilized in the training stage of novel solutions based on deep learning. Due to complicated data structure in retinal OCTs, a limited number of delineated OCT datasets are already available in presence of abnormalities; furthermore, the intrinsic three-dimensional (3D) structure of OCT is ignored in many public 2D datasets. We propose a new synthetic method, applicable to 3D data and feasible in presence of abnormalities like diabetic macular edema (DME). In this method, a limited number of OCT data is used during the training step and the Active Shape Model is used to produce synthetic OCTs plus delineation of retinal boundaries and location of abnormalities. Statistical comparison of thickness maps showed that synthetic dataset can be used as a statistically acceptable representative of the original dataset (p > 0.05). Visual inspection of the synthesized vessels was also promising. Regarding the texture features of the synthesized datasets, Q-Q plots were used, and even in cases that the points have slightly digressed from the straight line, the p-values of the Kolmogorov–Smirnov test rejected the null hypothesis and showed the same distribution in texture features of the real and the synthetic data. The proposed algorithm provides a unique benchmark for comparison of OCT enhancement methods and a tailored augmentation method to overcome the limited number of OCTs in deep learning algorithms.