Search

Congenital ocular malformations originate from defective morphogenesis during early eye development and cause 25% of childhood blindness. Formation of the eye is a multi-step, dynamic process; it involves evagination of the optic vesicle, followed by distal and ventral invagination, leading to the formation of a two-layered optic cup with a transient optic fissure. These tissue folding events require extensive changes in cell shape and tissue growth mediated by cytoskeleton mechanics and intercellular adhesion. We hypothesized that the Rho GTPase Cdc42 may be an essential, convergent effector downstream of key regulatory factors required for ocular morphogenesis. CDC42 controls actin remodeling, apicobasal polarity, and junction assembly. Here we identify a novel essential function for Cdc42 during eye morphogenesis in mouse; in Cdc42 mutant eyes expansion of the ventral optic cup is arrested, resulting in microphthalmia and a wide coloboma. Our analyses show that Cdc42 is required for expression of the polarity effector proteins PRKCZ and PARD6, intercellular junction protein tight junction protein 1, β -catenin, actin cytoskeleton F-actin, and contractile protein phospho myosin light chain 2. Expression of RPE fate determinants OTX2 and MITF, and formation of the RPE layer are severely affected in the temporal domain of the proximal optic cup. EdU incorporation is significantly downregulated. In addition, mitotic retinal progenitor cells mislocalize deeper, basal regions, likely contributing to decreased proliferation. We propose that morphogenesis of the ventral optic cup requires Cdc42 function for coordinated optic cup expansion and establishment of subretinal space, tissue tension, and differentiation of the ventral RPE layer. [ABSTRACT FROM AUTHOR]

Glaucoma refers to the accumulated loss of retinal cells within the optic nerve or the gradual visual loss caused by optic neuropathy. It is an illness that affects vision in the eye and is considered an irreversible condition that leads to degradation of eyesight. There are often no early warning signs of glaucoma, making it difficult to notice changes in vision due to subtle effects. To date, a large number of Deep Learning (DL) models have been developed for the accurate diagnosis of glaucoma. This work proposes an architecture for deep learning-based glaucoma detection using Convolutional Neural Networks (CNNs). CNNs can distinguish between patterns associated with glaucoma and non-glaucoma conditions, providing a hierarchical structure for classification. Using the proposed method, the disease is detected based on the optic cup-to-disc ratio. The diagnosis is further enhanced by integrating an image data generator for data augmentation. The results demonstrate that the proposed model achieved 98% accuracy, outperforming many existing algorithms. [ABSTRACT FROM AUTHOR]

Glaucoma, a multifactorial group of eye diseases characterized by progressive damage to the Glaucoma, a group of progressive optic neuropathies, is a leading cause of irreversible blindness globally, affecting millions of individuals. This research addresses the critical task of glaucoma identification through the segmentation of the optic disc and optic cup using ensemble learning algorithms, Unet and Gnet. The study leverages the capabilities of these algorithms to enhance the accuracy of the segmentation process, a crucial step in early glaucoma detection. A meticulously curated dataset of ophthalmic images is utilized, with a focus on preprocessing techniques includes resizing, normalization, filtering and contrast enhancement process to optimize the input quality. The proposed architectures of Unet and Gnet, highlighting their suitability for segmenting the optic disc and cup. The experimental setup involves rigorous training, with an emphasis on fine-tuning the models for segmentation tasks. Evaluation metrics, including Dice coefficient and sensitivity, are employed to assess the precision of the segmentation results. The outcomes demonstrate the efficacy of ensemble Unet and Gnet. Consistently achieving accuracy levels surpassing 98.90% across various datasets, the suggested model demonstrates exceptional performance in accurately categorizing severe cases. The study concludes with insights into the potential clinical impact of improved optic disc and cup segmentation on early glaucoma diagnosis, emphasizing the significance of ensemble learning in advancing ophthalmic image analysis for medical applications. [ABSTRACT FROM AUTHOR]

In this work, we aim to introduce some modifications to the Anam-Net deep neural network (DNN) model for segmenting optic cup (OC) and optic disc (OD) in retinal fundus images to estimate the cup-to-disc ratio (CDR). The CDR is a reliable measure for the early diagnosis of Glaucoma. In this study, we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images. Our DNN model is based on modifications to Anam-Net, incorporating an anamorphic depth embedding block. To reduce computational complexity, we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens. This modification significantly reduces the number of trainable parameters, making the model lightweight and suitable for resource-constrained applications. We evaluate the performance of the developed model using two publicly available retinal image databases, namely RIM-ONE and Drishti-GS. The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation. We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images, respectively. For OD segmentation using the RIM-ONE we obtain an f1-score (F1), Jaccard coefficient (JC), and overlapping error (OE) of 0.950, 0.9219, and 0.0781, respectively. Similarly, for OC segmentation using the same databases, we achieve scores of 0.8481 (F1), 0.7428 (JC), and 0.2572 (OE). Based on these experimental results and the significantly lower number of trainable parameters, we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR. [ABSTRACT FROM AUTHOR]

Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, ASNet, Swin-Unet, and TransUNet). [ABSTRACT FROM AUTHOR]

The retina consists of various cell types arranged in eight cell layers and two membranes that originate from the neuroectodermal cells. In this study, the timing of differentiation and distribution of the cellular components and the layers of the rabbit retina are investigated using light and electron microscopy and immunohistochemical techniques. There were 32 rabbit embryos and 12 rabbits used. The rabbit retina begins its prenatal development on the 10th day of gestation in the form of optic cup. The process of neuro‐ and gliogenesis occurs in several stages: In the first stage, the ganglionic cells are differentiated at the 15th day. The second stage includes the differentiation of Muller, amacrine, and cone cells on the 23rd day. The differentiation of bipolar, horizontal, and rod cells and formation of the inner segments of the photoreceptors consider the late stage that occurs by the 27th and 30th day of gestation. On the first week of age postnatally, the outer segments of the photoreceptors are developed. S100 protein is expressed by the Muller cells and its processes that traverse the retina from the outer to the inner limiting membranes. Calretinin is intensely labeled within the amacrine and displaced amacrine cells. Ganglionic cells exhibited moderate immunoreactivity for calretinin confined to their cytoplasm and dendrites. In conclusion, all stages of neuro‐ and gliogenesis of the rabbit retina occur during the embryonic period. Then, the retina continues its development postnatally by formation of the photoreceptor outer segments and all layers of the retina become established. Research Highlights: The aim of this study is to investigate the morphogenesis of the rabbit retina during pre‐ and postnatal life.The primordia of the retina could be observed in the form of the optic cup. The ganglionic cells are the first cells to differentiate, while the photoreceptor cells are the last.S100 protein is expressed by the Muller cells and its processes. Calretinin is intensely labeled in the amacrine and displaced amacrine cells and moderately expressed in the cytoplasm and dendrites of ganglionic cells. [ABSTRACT FROM AUTHOR]

Glaucoma is one of the leading causes of irreversible vision loss globally, often resulting in going blind. Early detection and treatment are critical in mitigating its impact, with retinal fundus imaging being the most common method for early screening. Traditionally, glaucoma is diagnosed by examining structural changes in these images, but this process heavily relies on the subjective judgment of clinicians, which can lead to errors. With the advent of artificial intelligence (AI), computer-aided diagnostic systems have emerged as powerful tools for early glaucoma screening, offering diagnostic accuracy comparable to that of expert ophthalmologists. Accurate glaucoma diagnosis from fundus images hinges on the precise calculation of the optic cup-to-disc ratio, which depends on the accurate segmentation of the optic cup and disc. Given that these regions occupy only a small portion of the fundus image, traditional deep learning methods typically approach segmentation in two stages: first, by locating the optic cup and disc through object detection, and then by performing fine-grained segmentation within the identified regions. However, the effectiveness of these methods is often constrained by the initial detection accuracy. In this paper, we introduce a novel one-stage segmentation framework, GlauSeg-Net, specifically designed for early glaucoma screening using retinal fundus images. Our method leverages a multi-task learning strategy that employs weak labels to pre-locate the segmentation target within feature layers, significantly enhancing the performance of small target segmentation. Experimental results demonstrate that proposed GlauSeg-Net get 96.8% and 88.3% segmentation accuracy on optic disc and optic cup respectively, and outperforms mainstream benchmark methods in segmentation accuracy.

Segmentation of retinal fragments like blood vessels, optic disc (OD), and optic cup (OC) enables the early detection of different retinal pathologies like diabetic retinopathy (DR), glaucoma, etc. This article proposed a novel deep learning architecture termed as Shell‐Net for the accurate segmentation of the retinal fragments. The main novelty of the architecture relies on the intellectual fusion of two different styled networks for attaining better segmentation results. The lower part of the Shell‐Net (feature condenser) follows the down‐sampling and up‐sampling style, whereas the upper part of the network (feature amplifier) follows the up‐sampling and down‐sampling style of architecture. In addition to this, an additional residual module (feature stabilizer) is integrated with the network to achieve more spatial information from lower levels. The lower part of the network reduces the data through summarization, enabling much scope for precise extraction of heavy details such as thick vessels, OD, and OC. On the contrary, the upper part of the network augments the data using duplication, assisting in the enlargement of minuscule details such as the tiny vessels and boundaries. Experiments were performed on publicly available datasets like Digital Retinal Images for Vessel Extraction (DRIVE), Child Heart and Health Study in England (CHASE_DB1), Structured Analysis of the Retina (STARE), Online Retinal Fundus Image Dataset for Glaucoma Analysis and Research (ORIGA), DRISHTI‐GS1, and Retinal Image database for Optic Nerve Evaluation (RIMONE r1). The network accomplished an average accuracy (ACC) and specificity (SPE) of 0.96 and 0.98 when tested on DRIVE, CHASE_DB1, and STARE datasets respectively for vessel segmentation. Furthermore, it outperformed previously existing models in OD and OC segmentation by achieving an average accuracy of 0.98 with a specificity of 0.99 on the DRISHTI_GS1 dataset. [ABSTRACT FROM AUTHOR]

Nowadays, retinal fundus images (RFI) play a crucial role in the early and exact prediction of glaucoma. Glaucoma is a vision-threatening eye illness that primarily impairs the eye nerves, and the serious condition of glaucoma leads to permanent vision loss. Several computer vision-based techniques have been explored in past studies for glaucoma detection, but those have certain misclassification results and are not much focused on early stage detection. This work presents an ensemble deep network (Ensemble DeepNet) model for the early detection of glaucoma via retinal fundus images. Initially, the kernel bilateral filter (kernel BF) is employed to improve the clarity of retinal images and reduces image noise. Then, the brightness of the input image is modified using linear histogram transformation (LHT). Further, the segmentation of the optic disc (OD) and optic cup (OC) is performed using twostage thresholds U-network (Two-stage TUNet). Finally, a new ensemble DeepNet model is developed based on VGGNet, DenseNet, and CapsNet models using a weighted majority voting technique. The improved aquila optimization (IAO) algorithm is used to find the optimal weights for the ensemble DeepNet model. The proposed ensemble DeepNet model attains the highest of 99.45%, which shows that the proposed ensemble model is highly suitable for predicting glaucoma disease in an early stage. [ABSTRACT FROM AUTHOR]

Segmentation is an essential requirement to accurately access diabetic retinopathy (DR) and it becomes extremely time-consuming and challenging to detect manually. As a result, an automatic retinal fundus image segmentation (RFIS) system is required to precisely define the region of interest and help ophthalmologists in the rapid diagnosis of DR. This systematic review provides a comprehensive overview of the development of deep learning (DL) based approach for RFIS to diagnose DR at an early stage. This review is fivefold: (1) retinal datasets, (2) pre-processing approaches, (3) DR segmentation and detection methods, (4) performance evaluation measures, and (5) proposed methodology. Articles on RFIS for DR detection were identified using the query "Deep Learning Techniques", "Diabetic Retinopathy", and "RFIS", alone and in combination using PubMed, Google Scholar, IEEE Xplore, and Research Gate databases until 2021 using PRISMA principle. Approximately 340 publications were searched and 115 relevant studies focused on the DL approaches for RFIS for DR diagnosis were chosen for study. According to the survey, 66% of researchers employed DL approaches for Blood vessel (BV) segmentation, 36% of researchers used DL approaches for lesion detection, and 15% of researchers used DL approaches for optic disc and optic cup (OD & OC) segmentation for DR Diagnosis. This systematic review provides detailed literature of the state-of-the-art relevant articles for RFIS of BV, Lesions, OD & OC for non-proliferative DR diagnosis and discusses future directions to improve the performance of DR and overcome research challenges. Finally, this article highlights the outline of the proposed work to improve the accuracy of existing models. [ABSTRACT FROM AUTHOR]

Glaucoma is currently leading retinal disease, which damages the eye because of the Intraocular pressure (IOP) on the eye. If glaucoma is left untreated it will lead to vision loss by damaging the Optic Nerve Head (ONH). The progression of glaucoma is examined on the retinal part of the eye by an experienced ophthalmologist. This approach is very tedious, and it consumes more time to do it manually. Hence this issue is right problem that can be solved by automatically diagnosing glaucoma with the help of the deep learning approaches. Convolutional Neural Networks (CNN's) are appropriate to find the solution for this type of issue as they can extract various levels of data from the input image, and which encourages to differentiate among non-glaucomic and glaucomic images. This proposed paper introduces an efficient glaucoma master framework to segment the optic cup and optic disc to find the Cup-to-Disc-Ratio (CDR). Here the diagnosis of glaucoma is achieved by using deep learning with novel CNN. The proposed system uses two individual CNN architecture to segment the Optic Cup (OC) and Optic Disc (OD) to get more accurate result. This model is trained and tested on DRISHTI – GS database, which is publicly available and an accuracy of 98% for optic disc and 97% for optic cup segmentation is achieved.

Glaucoma is an eye disease that causes loss of vision and blindness by damaging a nerve in the back of the eye called optic nerve. The optic nerve collects the visual information from the eyes and transmits to the brain. Glaucoma is mainly caused by an abnormal high pressure in the eyes. Over time, the increased pressure can erode the tissues of optic nerve, leading to vision loss or blindness. If it is diagnosed in advance, then only it can prevent the vision loss. To diagnose the glaucoma, it must accurately differentiate between the optic disc (OD), optic cup (OC), and the retinal nerve fiber layer (RNFL). The segmentation of the OD, OC, and RNFL remains a challenging issue under a minimum contrast image of boundaries. Therefore, in this study, an innovative method of Hybrid Symbiotic Differential Evolution Moth-Flame Optimization (SDMFO)-Multi-Boost Ensemble and Support Vector Machine (MBSVM)-based segmentation and classification framework is proposed for accurately detecting the glaucoma disease. By using Group Search Optimizer (GSO), the affected parts of the OD, OC and RNFL are segmented. The proposed SDMFO-MBSVM method is executed in MATLAB site, its performance is analyzed with three existing methods. From the comparison, the accuracy of the proposed method in OD segmentation gives better results of 3.37%, 4.54% and 2.22%, OC segmentation gives better results of 2.22%, 3.37% and 4.54%, and RNFL segmentation gives the better results of 3.37%, 97.21% and 5.74%. [ABSTRACT FROM AUTHOR]

In vitro differentiation of human pluripotent stem cells (hPSCs) into specialized tissues and organs represents a powerful approach to gain insight into those cellular and molecular mechanisms regulating human development. Although normal embryonic eye development is a complex process, generation of ocular organoids and specific ocular tissues from pluripotent stem cells has provided invaluable insights into the formation of lineage-committed progenitor cell populations, signal transduction pathways, and self-organization principles. This review provides a comprehensive summary of recent advances in generation of adenohypophyseal, olfactory, and lens placodes, lens progenitor cells and three-dimensional (3D) primitive lenses, "lentoid bodies", and "micro-lenses". These cells are produced alone or "community-grown" with other ocular tissues. Lentoid bodies/micro-lenses generated from human patients carrying mutations in crystallin genes demonstrate proof-of-principle that these cells are suitable for mechanistic studies of cataractogenesis. Taken together, current and emerging advanced in vitro differentiation methods pave the road to understand molecular mechanisms of cataract formation caused by the entire spectrum of mutations in DNA-binding regulatory genes, such as PAX6, SOX2, FOXE3, MAF, PITX3, and HSF4, individual crystallins, and other genes such as BFSP1, BFSP2, EPHA2, GJA3, GJA8, LIM2, MIP, and TDRD7 represented in human cataract patients. [ABSTRACT FROM AUTHOR]

Glaucoma is currently leading retinal disease, which damages the eye because of the Intraocular pressure (IOP) on the eye. If glaucoma is left untreated it will lead to vision loss by damaging the Optic Nerve Head (ONH). The progression of glaucoma is examined on the retinal part of the eye by an experienced ophthalmologist. This approach is very tedious, and it consumes more time to do it manually. Hence this issue is right problem that can be solved by automatically diagnosing glaucoma with the help of the deep learning approaches. Convolutional Neural Networks (CNN's) are appropriate to find the solution for this type of issue as they can extract various levels of data from the input image, and which encourages to differentiate among non-glaucomic and glaucomic images. This proposed paper introduces an efficient glaucoma master framework to segment the optic cup and optic disc to find the Cup-to-Disc-Ratio (CDR). Here the diagnosis of glaucoma is achieved by using deep learning with novel CNN. The proposed system uses two individual CNN architecture to segment the Optic Cup (OC) and Optic Disc (OD) to get more accurate result. This model is trained and tested on DRISHTI – GS database, which is publicly available and an accuracy of 98% for optic disc and 97% for optic cup segmentation is achieved. [ABSTRACT FROM AUTHOR]

Automatic and accurate segmentation of optic disc (OD) and optic cup (OC) in fundus images is a fundamental task in computer-aided ocular pathologies diagnosis. The complex structures, such as blood vessels and macular region, and the existence of lesions in fundus images bring great challenges to the segmentation task. Recently, the convolutional neural network-based methods have exhibited its potential in fundus image analysis. In this paper, we propose a cascaded two-stage network architecture for robust and accurate OD and OC segmentation in fundus images. In the first stage, the U-Net like framework with an improved attention mechanism and focal loss is proposed to detect accurate and reliable OD location from the full-scale resolution fundus images. Based on the outputs of the first stage, a refined segmentation network in the second stage that integrates multi-task framework and adversarial learning is further designed for OD and OC segmentation separately. The multi-task framework is conducted to predict the OD and OC masks by simultaneously estimating contours and distance maps as auxiliary tasks, which can guarantee the smoothness and shape of object in segmentation predictions. The adversarial learning technique is introduced to encourage the segmentation network to produce an output that is consistent with the true labels in space and shape distribution. We evaluate the performance of our method using two public retinal fundus image datasets (RIM-ONE-r3 and REFUGE). Extensive ablation studies and comparison experiments with existing methods demonstrate that our approach can produce competitive performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study proposes a novel glaucoma identification system from fundus images through the deep belief network (DBN) optimized by the elephant-herding optimization (EHO) algorithm. Initially, the input image undergoes the preprocessing steps of noise removal and enhancement processes, followed by optical disc (OD) and optical cup (OC) segmentation and extraction of structural, intensity, and textural features. Most discriminative features are then selected using the ReliefF algorithm and passed to the DBN for classification into glaucomatous or normal. To enhance the classification rate of the DBN, the DBN parameters are fine-tuned by the EHO algorithm. The model has experimented on public and private datasets with 7280 images, which attained a maximum classification rate of 99.4%, 100% specificity, and 99.89% sensitivity. The 10-fold cross validation reduced the misclassification and attained 98.5% accuracy. Investigations proved the efficacy of the proposed method in avoiding bias, dataset variability, and reducing false positives compared to similar works of glaucoma classification. The proposed system can be tested on diverse datasets, aiding in the improved glaucoma diagnosis. [ABSTRACT FROM AUTHOR]

Background: Pax2 is required for optic fissure development in many organisms, including humans and zebrafish. Zebrafish loss‐of‐function mutations in pax2a display coloboma, yet the etiology of the morphogenetic defects is unclear. Further, pax2 is duplicated in zebrafish, and a role for pax2b in optic fissure development has not been examined. Results: Using a combination of imaging and molecular genetics, we interrogated a potential role for pax2b and examined how loss of pax2 affects optic fissure development. Although optic fissure formation appears normal in pax2 mutants, an endothelial‐specific subset of periocular mesenchyme (POM) fails to initially localize within the optic fissure, yet both neural crest and endothelial‐derived POM ectopically accumulate at later stages in pax2a and pax2a; pax2b mutants. Apoptosis is not up‐regulated within the optic fissure in pax2 mutants, yet cell death is increased in tissues outside of the optic fissure, and when apoptosis is inhibited, coloboma is partially rescued. In contrast to pax2a, loss of pax2b does not appear to affect optic fissure morphogenesis. Conclusions: Our results suggest that pax2a, but not pax2b, supports cell survival outside of the optic fissure and POM abundance within it to facilitate optic fissure closure. Key Findings: Zebrafish pax2a null mutants display a defect in optic fissure closure and colobomaLoss of pax2b does not affect optic fissure developmentAn endothelial‐specific subset of periocular mesenchyme cells fails to initially localize to the optic fissure in pax2a mutantsAt a later stage of optic fissure development both neural crest and endothelial‐derived periocular mesenchyme ectopically accumulate within the optic fissurePax2a mutants have increased apoptosis in surrounding tissues, but not within the optic fissure margin cells, and apoptosis in part underlies the coloboma phenotype [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate the optic disc morphology in primary angle-closure glaucoma (PACG) versus primary open-angle glaucoma (POAG) in South Indians. Methods: A total of 60 patients (60 eyes) with PACG and 52 patients (52 eyes) with POAG were included in a cross-sectional observational study. The glaucoma diagnosis was based on a glaucomatous appearance of the optic disc correlating with visual field defects. The glaucoma was graded as early, moderate, or severe, depending upon perimetric loss. All patients underwent an ophthalmic evaluation, including visual field examination and planimetric analysis of 30° stereoscopic color optic disc photographs. Results: The POAG and PACG groups did not differ significantly in a disc or rim area, rim width, and frequencies of disc hemorrhages or rim notches. However, early POAG group (n = 15) had a significantly deeper cup depth (P = 0.01), larger beta zone (P = 0.01), and a higher frequency of localized retinal nerve fiber layer (RNFL) defects (P = 0.02) than early PACG (n = 20). Conclusion: In the early stage of the disease, POAG compared to PACG may be characterized by deeper disc cupping, a larger beta zone of peripapillary atrophy, and a higher frequency of localized RNFL defects. Such differences in early glaucoma may suggest differences in pathophysiology in POAG and PACG.

A central question in development biology is how a limited set of signalling pathways can instruct unlimited diversity of multicellular organisms. In this review, we use three ocular tissues as models of increasing complexity to present the astounding versatility of fibroblast growth factor (FGF) signalling. In the lacrimal gland, we highlight the specificity of FGF signalling in a one-dimensional model of budding morphogenesis. In the lens, we showcase the dynamics of FGF signalling in altering functional outcomes in a two-dimensional space. In the retina, we present the prolific utilization of FGF signalling from three-dimensional development to homeostasis. These examples not only shed light on the cellular basis for the perfection and complexity of ocular development, but also serve as paradigms for the diversity of FGF signalling.

BackgroundDuring development of the vertebrate eye, optic tissue is progressively compartmentalized into functionally distinct tissues. From the central to the peripheral optic cup, the original optic neuroepithelial tissue compartmentalizes, forming retina, ciliary body, and iris. The retina can be further sub-divided into peripheral and central compartments, where the central domain is specialized for higher visual acuity, having a higher ratio and density of cone photoreceptors in most species.ResultsClassically, models depict a segregation of the early optic cup into only two domains, neural and non-neural. Recent studies, however, uncovered discrete precursors for central and peripheral retina in the optic vesicle, indicating that the neural retina cannot be considered as a single unit with homogeneous specification and development. Instead, central and peripheral retina may be subject to distinct developmental pathways that underlie their specialization.ConclusionsThis review focuses on lineage relationships in the retina and revisits the historical context for segregation of central and peripheral retina precursors before overt eye morphogenesis.

The vertebrate eye primordium consists of a pseudostratified neuroepithelium, the optic vesicle (OV), in which cells acquire neural retina or retinal pigment epithelium (RPE) fates. As these fates arise, the OV assumes a cup shape, influenced by mechanical forces generated within the neural retina. Whether the RPE passively adapts to retinal changes or actively contributes to OV morphogenesis remains unexplored. We generated a zebrafish Tg(E1-bhlhe40:GFP) line to track RPE morphogenesis and interrogate its participation in OV folding. We show that, in virtual absence of proliferation, RPE cells stretch and flatten, thereby matching the retinal curvature and promoting OV folding. Localized interference with the RPE cytoskeleton disrupts tissue stretching and OV folding. Thus, extreme RPE flattening and accelerated differentiation are efficient solutions adopted by fast-developing species to enable timely optic cup formation. This mechanism differs in amniotes, in which proliferation drives RPE expansion with a much-reduced need of cell flattening.

Glaucoma is an ailment causing permanent vision loss but can be prevented through the early detection. Optic disc to cup ratio is one of the key factors for glaucoma diagnosis. But accurate segmentation of disc and cup is still a challenge. To mitigate this challenge, an effective system for optic disc and cup segmentation using deep learning architecture is presented in this paper. Modified Groundtruth is utilized to train the proposed model. It works as fused segmentation marking by multiple experts that helps in improving the performance of the system. Extensive computer simulations are conducted to test the efficiency of the proposed system. For the implementation three standard benchmark datasets such as DRISHTI-GS, DRIONS-DB and RIM-ONE v3 are used. The performance of the proposed system is validated against the state-of-the-art methods. Results indicate an average overlapping score of 96.62%, 96.15% and 98.42% respectively for optic disc segmentation and an average overlapping score of 94.41% is achieved on DRISHTI-GS which is significant for optic cup segmentation. [ABSTRACT FROM AUTHOR]

Recently, automated retinal image processing has been considered a competitive field of research due to the low-accuracy results, complexity, and unacceptable outcomes associated with it. In this article, we proposed a novel approach for the classification of fundus images from different kinds of fundus disorders. The original images are preprocessed in terms of noise and contrast enhancement by using the contrast limited adaptive histogram equalization method. The optic cup segmentation from the fundus images is effectively handled via the search and rescue optimization algorithm. After that, the color, texture, and shape-based gray-level co-occurrence matrix features are extracted. The hybrid particle swarm optimization with local search strategy improves the DNN parameter and the newly developed method is named as optimal DNN. The optimal DNN is used to classify whether the image is diabetic retinopathy, glaucoma, or age-related macular degeneration. Experimentally, different kinds of datasets such as STARE, Drishti, and RIM-One datasets with performance measure are validated. Finally, the proposed approaches demonstrate higher classification performances in terms of accuracy, specificity, sensitivity, precision, recall, and f-measure. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate the optic disc morphology in primary angle-closure glaucoma (PACG) versus primary open-angle glaucoma (POAG) in South Indians.Methods: A total of 60 patients (60 eyes) with PACG and 52 patients (52 eyes) with POAG were included in a cross-sectional observational study. The glaucoma diagnosis was based on a glaucomatous appearance of the optic disc correlating with visual field defects. The glaucoma was graded as early, moderate, or severe, depending upon perimetric loss. All patients underwent an ophthalmic evaluation, including visual field examination and planimetric analysis of 30° stereoscopic color optic disc photographs.Results: The POAG and PACG groups did not differ significantly in a disc or rim area, rim width, and frequencies of disc hemorrhages or rim notches. However, early POAG group (n = 15) had a significantly deeper cup depth (P = 0.01), larger beta zone (P = 0.01), and a higher frequency of localized retinal nerve fiber layer (RNFL) defects (P = 0.02) than early PACG (n = 20).Conclusion: In the early stage of the disease, POAG compared to PACG may be characterized by deeper disc cupping, a larger beta zone of peripapillary atrophy, and a higher frequency of localized RNFL defects. Such differences in early glaucoma may suggest differences in pathophysiology in POAG and PACG. [ABSTRACT FROM AUTHOR]

Glaucoma is a chronic optic neuropathy. It was predicted that people with bilateral blindness caused by glaucoma will increase each year. Hence, computer-aided diagnosis of glaucoma was proposed to assist ophthalmologist to conduct a fast and accurate glaucoma screening. One of the ocular examination in screening is optic nerve examination called disc damage likelihood scale (DDLS). It is important to find the optic disc and the optic cup to determine the narrowest width of the neuroretinal rim when using DDLS. To find the optic cup, this study proposed a segmentation scheme consisting of pre-process, segmentation, convex hull and morphological opening operation. In pre-process the blood vessel was removed to make the segmentation process of the optic cup easier. The segmentation process was done by using an adaptive thresholding followed by morphological image processing such as convex hull, opening and erosion. This algorithm was applied on Magrabia dataset and attained accuracy, specificity and sensitivity of 99.50%, 99.75% and 75.19% respectively.

Glaucoma is an eye disease which is one of the most common causes of blindness. Accurate optic disc (OD) and optic cup (OC) segmentation play a critical role for detecting glaucoma. Considering that the existing approaches can't effectively integrate the multi-view information deriving from shape and appearance to sufficiently describe OD and OC for segmentation, Locally Statistical Active Contour Model with the Information of Appearance and Shape (LSACM-AS) and Modified Locally Statistical Active Contour Model with the Information of Appearance and Shape (MLSACM-AS) are proposed in this paper. The main contributions are as below: (1) we introduce the Locally Statistical Active Contour Model (LSACM) to address the commonly occurred intensity inhomogeneity phenomenon caused by imperfection of image devices or illumination variations. (2) In order to overcome the common effects caused by pathological changes (i.e., peripapillary atrophy (PPA)) and vessel occlusion in OD and OC segmentation, we integrate the local image probability information around the point of interest from a multi-dimensional feature space into our model to preserve the integrity of the OD and OC structures. (3) Since the segmentation objects have the similar ellipse shape structure, we incorporate the shape priori constraint information into our model to further improve the robustness of the variations found in and around objects regions. To evaluate the effectiveness of the proposed models, an available publicly DRISHTI-GS database is employed in this paper. Seen from the abundant experiments, the proposed models outperform the state-of-the-art approaches in terms of the obtained qualitative and quantitative results.

Deep learning is a useful technique for investigating the medicinal images. Glaucoma is a neurotic condition, dynamic neuro degeneration of the optic nerve, which leads visual impairment. It could be forestalled by an early detection of glaucoma and the regular screening with specialist for glaucoma diagnosis. Glaucoma is assessed by observing intra ocular pressure and optic Cup-Disc-Ratio (CDR). In this paper, novel mechanized glaucoma recognition has been performed by utilizing computer supported analysis from fundus images. The simulation outcomes are acquired by utilizing a Support Vector Machine based VGG-19 network architecture. The CDR threshold value of 0.41 has been used for glaucoma recognition. The fundus images which has the CDR greater than 0.41 is treated as glaucoma affected and less than 0.41 is non-glaucoma fundus images. The proposed glaucoma recognition system works with reasonable to obtain and generally utilized digital color fundus images. For the set of 175 fundus images a classification precision of 94% has been accomplished. [ABSTRACT FROM AUTHOR]