Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review

Glaucoma is one of the primary causes of vision loss around the world, necessitating accurate and efficient detection methods. Traditional manual detection approaches have limitations in terms of cost, time, and subjectivity. Recent developments in deep learning approaches demonstrate potential in automating glaucoma detection by detecting relevant features from retinal fundus images. This article provides a comprehensive overview of cutting-edge deep learning methods used for the segmentation, classification, and detection of glaucoma. By analyzing recent studies, the effectiveness and limitations of these techniques are evaluated, key findings are highlighted, and potential areas for further research are identified. The use of deep learning algorithms may significantly improve the efficacy, usefulness, and accuracy of glaucoma detection. The findings from this research contribute to the ongoing advancements in automated glaucoma detection and have implications for improving patient outcomes and reducing the global burden of glaucoma

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions of different perceptual levels, we propose to let them interact by the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise segmentation and the class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate semantic segmentation, even though the number of the images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all three challenges.Comment: IEEE Transactions on Medical Imagin

Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data

Deep learning-based segmentation methods have been widely employed for automatic glaucoma diagnosis and prognosis. In practice, fundus images obtained by different fundus cameras vary significantly in terms of illumination and intensity. Although recent unsupervised domain adaptation (UDA) methods enhance the models' generalization ability on the unlabeled target fundus datasets, they always require sufficient labeled data from the source domain, bringing auxiliary data acquisition and annotation costs. To further facilitate the data efficiency of the cross-domain segmentation methods on the fundus images, we explore UDA optic disc and cup segmentation problems using few labeled source data in this work. We first design a Searching-based Multi-style Invariant Mechanism to diversify the source data style as well as increase the data amount. Next, a prototype consistency mechanism on the foreground objects is proposed to facilitate the feature alignment for each kind of tissue under different image styles. Moreover, a cross-style self-supervised learning stage is further designed to improve the segmentation performance on the target images. Our method has outperformed several state-of-the-art UDA segmentation methods under the UDA fundus segmentation with few labeled source data.Comment: Accepted by The 33rd British Machine Vision Conference (BMVC) 202

A Comparison of Deep Learning Techniques for Glaucoma Diagnosis on Retinal Fundus Images

Glaucoma is one of the serious disorders which cause permanent vision loss if it left undetected. The primary cause of the disease is elevated intraocular pressure, impacting the optic nerve head (ONH) that originates from the optic disc. The variation in optic disc to optic cup ratio helps in early detection of the disease. Manual calculation of Cup to Disc Ratio (CDR) consumes more time and the prediction is also not accurate. Utilizing deep learning for the automatic detection of glaucoma facilitates precise and early identification, significantly enhancing the accuracy of glaucoma detection. The deep learning technique initiates the process by initially pre-processing the image to achieve data augmentation, followed by the segmentation of the optic disc and optic cup from the retinal fundus image. From the segmented Optic Disc (OD)and Optic Cup (OC) feature are selected and CDR calculated. Based on the CDR value the Glaucoma classification is performed. Various deep learning techniques like CNN, transfer learning, algorithm was proposed in early detection of glaucoma. From the comparative analysis glaucoma diagnosis, the proposed deep learning artifact Convolutional Neural Network outperform in early diagnosis of glaucoma providing accuracy of 99.3 8%

EDDense-Net: Fully Dense Encoder Decoder Network for Joint Segmentation of Optic Cup and Disc

Glaucoma is an eye disease that causes damage to the optic nerve, which can lead to visual loss and permanent blindness. Early glaucoma detection is therefore critical in order to avoid permanent blindness. The estimation of the cup-to-disc ratio (CDR) during an examination of the optical disc (OD) is used for the diagnosis of glaucoma. In this paper, we present the EDDense-Net segmentation network for the joint segmentation of OC and OD. The encoder and decoder in this network are made up of dense blocks with a grouped convolutional layer in each block, allowing the network to acquire and convey spatial information from the image while simultaneously reducing the network's complexity. To reduce spatial information loss, the optimal number of filters in all convolution layers were utilised. In semantic segmentation, dice pixel classification is employed in the decoder to alleviate the problem of class imbalance. The proposed network was evaluated on two publicly available datasets where it outperformed existing state-of-the-art methods in terms of accuracy and efficiency. For the diagnosis and analysis of glaucoma, this method can be used as a second opinion system to assist medical ophthalmologists