Optic Disc Segmentation Research Articles

Glaucoma Detection Using Multiple Feature Set With Recurrent Neural Network

Abstract One of the most widespread illnesses of blindness is glaucoma. Optic nerve are essentialfor clear vision, but glaucoma effects the optic nerves and results blurred vision. This condition is often exacerbated by abnormally high intra-ocular pressure. Accurate early identification and continuous screening can help to minimize loss of vision. A non-invasive computer-aided diagnosis treatment uses optical fundus images to detect glaucoma in its early stages. This work includes image preprocessing, optic disk (OD) segmentation, feature extraction from the OD and recurrent neural network classification to identify glaucoma. The performance of the proposed system is tested using fundus image datasets such as DRISHTI-GS and Large-Scale Attention-Based Glaucoma (LAG). By this method, glaucoma detection accuracyof 96.1% is obtained for DRISHTI-GS and 92.73% for LAG dataset, which is higher thanthe existing state of arts. Proposed procedure can help ophthalmologists diagnose glaucomawith good performance.

AI-Based Automatic Detection and Classification of Diabetic Retinopathy Using U-Net and Deep Learning

Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-related retinal vascular disease is one of the world’s most common leading causes of blindness and vision impairment. Therefore, automated DR detection systems would greatly benefit the early screening and treatment of DR and prevent vision loss caused by it. Researchers have proposed several systems to detect abnormalities in retinal images in the past few years. However, Diabetic Retinopathy automatic detection methods have traditionally been based on hand-crafted feature extraction from the retinal images and using a classifier to obtain the final classification. DNN (Deep neural networks) have made several changes in the previous few years to assist overcome the problem mentioned above. We suggested a two-stage novel approach for automated DR classification in this research. Due to the low fraction of positive instances in the asymmetric Optic Disk (OD) and blood vessels (BV) detection system, preprocessing and data augmentation techniques are used to enhance the image quality and quantity. The first step uses two independent U-Net models for OD (optic disc) and BV (blood vessel) segmentation. In the second stage, the symmetric hybrid CNN-SVD model was created after preprocessing to extract and choose the most discriminant features following OD and BV extraction using Inception-V3 based on transfer learning, and detects DR by recognizing retinal biomarkers such as MA (microaneurysms), HM (hemorrhages), and exudates (EX). On EyePACS-1, Messidor-2, and DIARETDB0, the proposed methodology demonstrated state-of-the-art performance, with an average accuracy of 97.92%, 94.59%, and 93.52%, respectively. Extensive testing and comparisons with baseline approaches indicate the efficacy of the suggested methodology.

Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images

Glaucoma is one of the most common chronic diseases that may lead to irreversible vision loss. The number of patients with permanent vision loss due to glaucoma is expected to increase at an alarming rate in the near future. A considerable amount of research is being conducted on computer-aided diagnosis for glaucoma. Segmentation of the optic cup (OC) and optic disc (OD) is usually performed to distinguish glaucomatous and non-glaucomatous cases in retinal fundus images. However, the OC boundaries are quite non-distinctive; consequently, the accurate segmentation of the OC is substantially challenging, and the OD segmentation performance also needs to be improved. To overcome this problem, we propose two networks, separable linked segmentation network (SLS-Net) and separable linked segmentation residual network (SLSR-Net), for accurate pixel-wise segmentation of the OC and OD. In SLS-Net and SLSR-Net, a large final feature map can be maintained in our networks, which enhances the OC and OD segmentation performance by minimizing the spatial information loss. SLSR-Net employs external residual connections for feature empowerment. Both proposed networks comprise a separable convolutional link to enhance computational efficiency and reduce the cost of network. Even with a few trainable parameters, the proposed architecture is capable of providing high segmentation accuracy.The segmentation performances of the proposed networks were evaluated on four publicly available retinal fundus image datasets: Drishti-GS, REFUGE, Rim-One-r3, and Drions-DB which confirmed that our networks outperformed the state-of-the-art segmentation architectures.

Graph deep network for optic disc and optic cup segmentation for glaucoma disease using retinal imaging.

The fundus imaging method of eye screening detects eye diseases by segmenting the optic disc (OD) and optic cup (OC). OD and OC are still challenging to segment accurately. This work proposes three-layer graph-based deep architecture with an enhanced fusion method for OD and OC segmentation. CNN encoder-decoder architecture, extended graph network, and approximation via fusion-based rule are explored for connecting local and global information. A graph-based model is developed for combining local and overall knowledge. By extending feature masking, regularization of repetitive features with fusion for combining channels has been done. The performance of the proposed network is evaluated through the analysis of different metric parameters such as dice similarity coefficient (DSC), intersection of union (IOU), accuracy, specificity, sensitivity. Experimental verification of this methodology has been done using the four benchmarks publicly available datasets DRISHTI-GS, RIM-ONE for OD, and OC segmentation. In addition, DRIONS-DB and HRF fundus imaging datasets were analyzed for optimizing the model's performance based on OD segmentation. DSC metric of methodology achieved 0.97 and 0.96 for DRISHTI-GS and RIM-ONE, respectively. Similarly, IOU measures for DRISHTI-GS and RIM-ONE datasets were 0.96 and 0.93, respectively, for OD measurement. For OC segmentation, DSC and IOU were measured as 0.93 and 0.90 respectively for DRISHTI-GS and 0.83 and 0.82 for RIM-ONE data. The proposed technique improved value of metrics with most of the existing methods in terms of DSC and IOU of the results metric of the experiments for OD and OC segmentation.

Leveraging non-expert crowdsourcing to segment the optic cup and disc of multicolor fundus images.

Multicolor scanning laser imaging (MCI) images have broad application potential in the diagnosis of fundus diseases such as glaucoma. However, the performance level of automatic aided diagnosis systems based on MCI images is limited by the lack of high-quality annotations of numerous images. Producing annotations for vast amounts of MCI images will be a prolonged process if we only employ experts. Therefore, we consider non-expert crowdsourcing, which is an alternative approach to produce useful annotations efficiently and low cost. In this work, we aim to explore the effectiveness of non-expert crowdsourcing on the segmentation of the optic cup (OC) and optic disc (OD), which is an upstream task for glaucoma diagnosis, using MCI images. To this end, desensitized MCI images are independently annotated by four non-expert annotators, constructing a crowdsourcing dataset. To profit from crowdsourcing, we propose a model consisting of coupled regularization network and segmentation network. The regularization network generates learnable pixel-wise confusion matrices (CMs) that reflects preferences of each annotator. During training, the CMs and segmentation network are simultaneously optimized to enable dynamic trade-offs for non-expert annotations and generate reliable predictions. Crowdsourcing learning using our method have an average Mean Intersection Over Union ( ) of 91.34%, while the average of model trained by expert annotations is 91.72%. In addition, comparative experiments show that in our segmentation task non-expert crowdsourcing can be on a par with the expert who annotates 90% of data. Our work suggests that crowdsourcing in the segmentation of OC and OD using MCI images has the potential to be a substitute to expert annotation, which will accelerate the construction of large datasets to facilitate the application of deep learning in clinical diagnosis using MCI images.

Particle Swarm Optimization-Based Approach for Optic Disc Segmentation.

Fundus segmentation is an important step in the diagnosis of ophthalmic diseases, especially glaucoma. A modified particle swarm optimization algorithm for optic disc segmentation is proposed, considering the fact that the current public fundus datasets do not have enough images and are unevenly distributed. The particle swarm optimization algorithm has been proved to be a good tool to deal with various extreme value problems, which requires little data and does not require pre-training. In this paper, the segmentation problem is converted to a set of extreme value problems. The scheme performs data preprocessing based on the features of the fundus map, reduces noise on the picture, and simplifies the search space for particles. The search space is divided into multiple sub-search spaces according to the number of subgroups, and the particles inside the subgroups search for the optimal solution in their respective sub-search spaces. The gradient values are used to calculate the fitness of particles and contours. The entire group is divided into some subgroups. Every particle flies in their exploration for the best solution. During the iteration, particles are not only influenced by local and global optimal solutions but also additionally attracted by particles between adjacent subgroups. By collaboration and information sharing, the particles are capable of obtaining accurate disc segmentation. This method has been tested with the Drishti-GS and RIM-ONE V3 dataset. Compared to several state-of-the-art methods, the proposed method substantially improves the optic disc segmentation results on the tested datasets, which demonstrates the superiority of the proposed work.

Classification of Glaucoma Based on Elephant-Herding Optimization Algorithm and Deep Belief Network

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.

Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network.

Glaucoma is one of the leading factors of vision loss, where the people tends to lose their vision quickly. The examination of cup-to-disc ratio is considered essential in diagnosing glaucoma. It is hence regarded that the segmentation of optic disc and cup is useful in finding the ratio. In this paper, we develop an extraction and segmentation of optic disc and cup from an input eye image using modified recurrent neural networks (mRNN). The mRNN use the combination of recurrent neural network (RNN) with fully convolutional network (FCN) that exploits the intra- and interslice contexts. The FCN extracts the contents from an input image by constructing a feature map for the intra- and interslice contexts. This is carried out to extract the relevant information, where RNN concentrates more on interslice context. The simulation is conducted to test the efficacy of the model that integrates the contextual information for optimal segmentation of optical cup and disc. The results of simulation show that the proposed method mRNN is efficient in improving the rate of segmentation than the other deep learning models like Drive, STARE, MESSIDOR, ORIGA, and DIARETDB.

Identifying Those at Risk of Glaucoma: A Deep Learning Approach for Optic Disc and Cup Segmentation and Their Boundary Analysis.

Glaucoma is a leading cause of irreversible vision loss that gradually damages the optic nerve. In ophthalmic fundus images, measurements of the cup to optic disc (CD) ratio, CD area ratio, neuroretinal rim to optic disc (RD) area ratio, and rim thickness are key measures to screen for potential glaucomatous damage. We propose an automatic method using deep learning algorithms to segment the optic disc and cup and to estimate the key measures. The proposed method comprises three steps: The Region of Interest (ROI) (location of the optic disc) detection from a fundus image using Mask R-CNN, the optic disc and cup segmentation from the ROI using the proposed Multiscale Average Pooling Net (MAPNet), and the estimation of the key measures. Our segmentation results using 1099 fundus images show 0.9381 Jaccard Index (JI) and 0.9679 Dice Coefficient (DC) for the optic disc and 0.8222 JI and 0.8996 DC for the cup. The average CD, CD area, and RD ratio errors are 0.0451, 0.0376, and 0.0376, respectively. The average disc, cup, and rim radius ratio errors are 0.0500, 0.2257, and 0.2166, respectively. Our method performs well in estimating the key measures and shows potential to work within clinical pathways once fully implemented.

AFENet: Attention Fusion Enhancement Network for Optic Disc Segmentation of Premature Infants.

Retinopathy of prematurity and ischemic brain injury resulting in periventricular white matter damage are the main causes of visual impairment in premature infants. Accurate optic disc (OD) segmentation has important prognostic significance for the auxiliary diagnosis of the above two diseases of premature infants. Because of the complexity and non-uniform illumination and low contrast between background and the target area of the fundus images, the segmentation of OD for infants is challenging and rarely reported in the literature. In this article, to tackle these problems, we propose a novel attention fusion enhancement network (AFENet) for the accurate segmentation of OD in the fundus images of premature infants by fusing adjacent high-level semantic information and multiscale low-level detailed information from different levels based on encoder–decoder network. Specifically, we first design a dual-scale semantic enhancement (DsSE) module between the encoder and the decoder inspired by self-attention mechanism, which can enhance the semantic contextual information for the decoder by reconstructing skip connection. Then, to reduce the semantic gaps between the high-level and low-level features, a multiscale feature fusion (MsFF) module is developed to fuse multiple features of different levels at the top of encoder by using attention mechanism. Finally, the proposed AFENet was evaluated on the fundus images of preterm infants for OD segmentation, which shows that the proposed two modules are both promising. Based on the baseline (Res34UNet), using DsSE or MsFF module alone can increase Dice similarity coefficients by 1.51 and 1.70%, respectively, whereas the integration of the two modules together can increase 2.11%. Compared with other state-of-the-art segmentation methods, the proposed AFENet achieves a high segmentation performance.

Joint optic disk and cup segmentation for glaucoma screening using a region-based deep learning network.

To develop and validate an end-to-end region-based deep convolutional neural network (R-DCNN) to jointly segment the optic disc (OD) and optic cup (OC) in retinal fundus images for precise cup-to-disc ratio (CDR) measurement and glaucoma screening. In total, 2440 retinal fundus images were retrospectively obtained from 2033 participants. An R-DCNN was presented for joint OD and OC segmentation, where the OD and OC segmentation problems were formulated into object detection problems. We compared R-DCNN's segmentation performance on our in-house dataset with that of four ophthalmologists while performing quantitative, qualitative and generalization analyses on the publicly available both DRISHIT-GS and RIM-ONE v3 datasets. The Dice similarity coefficient (DC), Jaccard coefficient (JC), overlapping error (E), sensitivity (SE), specificity (SP) and area under the curve (AUC) were measured. On our in-house dataset, the proposed model achieved a 98.51% DC and a 97.07% JC for OD segmentation, and a 97.63% DC and a 95.39% JC for OC segmentation, achieving a performance level comparable to that of the ophthalmologists. On the DRISHTI-GS dataset, our approach achieved 97.23% and 94.17% results in DC and JC results for OD segmentation, respectively, while it achieved a 94.56% DC and an 89.92% JC for OC segmentation. Additionally, on the RIM-ONE v3 dataset, our model generated DC and JC values of 96.89% and 91.32% on the OD segmentation task, respectively, whereas the DC and JC values acquired for OC segmentation were 88.94% and 78.21%, respectively. The proposed approach achieved very encouraging performance on the OD and OC segmentation tasks, as well as in glaucoma screening. It has the potential to serve as a useful tool for computer-assisted glaucoma screening.

Joint segmentation of optic cup and optic disc using deep convolutional generative adversarial network

Glaucoma, as one of the three major blinding ophthalmic diseases in the world, is usually accompanied by changes in the structure of the patient’s optic disc, such as optic disc atrophy and depression. Clinical ophthalmologists tend to use the cup-disc ratio as an evaluation index to realize the screening and diagnosis of glaucoma. Therefore, the accurate measurement of optic cup (OC), optic disc (OD) and other parameters is of great clinical significance for early screening of glaucoma. Inspired by game theory, this paper combines deep convolutional neural networks (DCNN) with generative adversarial networks (GAN), and proposes a model for the joint segmentation of OC and OD. Specifically, the generator is composed of a deep convolutional encoder-decoder network to jointly segment the OC and OD, and the discriminator is composed of an eight layer full convolutional neural network. The discrimination results adjust the parameters in the structure of the generator and discriminator network through back propagation to achieve the effect of autonomous learning and optimization of the model. When the proposed network and the existing networks are evaluated on the public dataset Drishti-GS1, the research results demonstrate that the proposed network can achieve a significant improvement in the overall performance.

Robust color medical image segmentation on unseen domain by randomized illumination enhancement

Owing to the data distribution shifts generated by collecting images using various imaging protocols and device vendors, the generalization capability of deep models is crucial for medical image analysis when applied to test datasets in clinical environments. Domain generalization (DG) methods have shown promising generalization performance in the field of medical image segmentation. In contrast to conventional DG, which has strict requirements regarding the availability of multiple source domains, we consider a more challenging problem, that is, single-domain generalization (SDG), where only a single source is available during network training. In this scenario, the augmentation of the entire image to improve the model generalization ability may cause alteration of hue values, resulting in the wrong segmentation of tissues in color medical images. To resolve this problem, we first present a novel illumination-randomized SDG framework to improve the model generalization power for color medical image segmentation by synthesizing randomized illumination maps. Specifically, we devise unsupervised retinex-based image decomposition neural networks (ID-Nets) to decompose color medical images into reflectance and illumination maps. Illumination maps are augmented by performing illumination randomization to generate medical color images under diverse illumination conditions. Second, to measure the quality of retinex-based image decomposition, we devise a novel metric, the transport gradient consistency index, by modeling physical illumination. Extensive experiments are performed to evaluate our proposed framework on two retinal fundus image segmentation tasks: optic cup and disc segmentation. The experimental results demonstrate that our framework outperforms other SDG and image enhancement methods, surpassing the state-of-the-art SDG methods by up to 9.6% with respect to the Dice coefficient.

Automatic detection and segmentation of optic disc using a modified convolution network

Diabetic Retinopathy refers to an eye disease commonly found in patients suffering from prolonged diabetes mellitus and can eventually cause blindness resulting from leaking of retinal blood vessels. Early detection of any abnormalities in the optic disc can help the ophthalmologists in effective treatment of the patient. Segmentation of optic disc is also necessary for screening glaucoma using optic cup-to-disc ratio. Automatic screening of fundus images using computerized techniques can be of great help in detection of several retinal pathologies. The study presents a deep learning approach for detecting and segmenting the optic disc automatically from the fundus images using a modified convolutional network. The most powerful encoder structure has been identified by experimenting on seven encoder networks namely VGG11, VGG13, VGG16, VGG19, Resnet34, Densenet121 and InceptionV3. In this paper, the VGG16 framework is considered as an encoder, while the decoder is designed as a symmetric structure of that of the encoder to perform object segmentation more effectively. The Convolutional Long Short Term Memory is incorporated in the encoder framework for better convergence and to obtain high accuracy. The efficiency of this work is verified by applying it over a large variation of fundus images from databases namely MESSIDOR, DRIVE, DIARETDB0, DIARETDB1, CHASE-DB1, IDRiD and STARE. Intersection-over-Union, Dice Coefficient, Accuracy and Sensitivity have been computed for evaluating the effectiveness of the work. This algorithm has attained an overall accuracy of 99.44%. The experimental results indicate the superiority of this model on segmentation operation.

Optic Disk Segmentation Using Histogram Analysis

In the field of disease diagnosis with ophthalmic aids, automatic segmentation of the retinal optic disc is required. The main challenge in OD segmentation is to determine the exact location of the OD and remove noise in the retinal image. This paper proposes a method for automatic optical disc segmentation on color retinal fundus images using histogram analysis. Based on the properties of the optical disk, where the optical disk tends to occupy a high intensity. This method has been applied to the Digital Retinal Database for Vessel Extraction (DRIVE)and MESSIDOR database. The experimental results show that the proposed automatic optical segmentation method has an accuracy of 55% for DRIVE dataset and 89% for MESSIDOR database

Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation - A Review.

BackgroundGlobally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs.MethodsA systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed.ResultsEight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation.ConclusionWe provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.

Beyond Mutual Information: Generative Adversarial Network for Domain Adaptation Using Information Bottleneck Constraint.

Medical images from multicentres often suffer from the domain shift problem, which makes the deep learning models trained on one domain usually fail to generalize well to another. One of the potential solutions for the problem is the generative adversarial network (GAN), which has the capacity to translate images between different domains. Nevertheless, the existing GAN-based approaches are prone to fail at preserving image-objects in image-to-image (I2I) translation, which reduces their practicality on domain adaptation tasks. In this regard, a novel GAN (namely IB-GAN) is proposed to preserve image-objects during cross-domain I2I adaptation. Specifically, we integrate the information bottleneck constraint into the typical cycle-consistency-based GAN to discard the superfluous information (e.g., domain information) and maintain the consistency of disentangled content features for image-object preservation. The proposed IB-GAN is evaluated on three tasks-polyp segmentation using colonoscopic images, the segmentation of optic disc and cup in fundus images and the whole heart segmentation using multi-modal volumes. We show that the proposed IB-GAN can generate realistic translated images and remarkably boost the generalization of widely used segmentation networks (e.g., U-Net).

Glaucoma Detection Using Image Processing and Supervised Learning for Classification.

A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method's initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.

A Novel Deep Learning Algorithm for Optical Disc Segmentation for Glaucoma Diagnosis

In India, first major cause of blindness is the cataract and the next major cause of blindness is the glaucoma which is approximately 11.9 million per yearly. The Optical Nerve Head (ONH) misalignment is the initial symptom which helps in predicting glaucoma in early stage. The optic cup and optic disc misalignment cause variation in Cup to Disc Ratio (CDR). Accurate segmentation of optic disc and cup is needed in order to calculate CDR properly. Manual segmentation can be automated to improve accuracy. Several deep learning algorithms are proposed to improve segmentation of optic cup and disc, still segmentation becomes difficult because of intersection of cup and disc. Here a Modified U-net model is proposed, which locate the optic disc in retinal fundus image, after that disc and cup segmentation is performed to calculate the CDR also the existing algorithm like adaptive thresholding, U-net model results are compared with the proposed model. The proposed and the existing methods are evaluated on three different publicly available dataset RIM-ONE, DRIONS-DB and Drishti-GS1.

Weak label based Bayesian U-Net for optic disc segmentation in fundus images

Fundus images have been widely used in routine examinations of ophthalmic diseases. For some diseases, the pathological changes mainly occur around the optic disc area; therefore, detection and segmentation of the optic disc are critical pre-processing steps in fundus image analysis. Current machine learning based optic disc segmentation methods typically require manual segmentation of the optic disc for the supervised training. However, it is time consuming to annotate pixel-level optic disc masks and inevitably induces inter-subject variance. To address these limitations, we propose a weak label based Bayesian U-Net exploiting Hough transform based annotations to segment optic discs in fundus images. To achieve this, we build a probabilistic graphical model and explore a Bayesian approach with the state-of-the-art U-Net framework. To optimize the model, the expectation-maximization algorithm is used to estimate the optic disc mask and update the weights of the Bayesian U-Net, alternately. Our evaluation demonstrates strong performance of the proposed method compared to both fully- and weakly-supervised baselines.