Accurate Optic Disc Segmentation Research Articles

W-Net: A boundary-aware cascade network for robust and accurate optic disc segmentation

Accurate optic disc (OD) segmentation has a great significance for computer-aided diagnosis of different types of eye diseases. Due to differences in image acquisition equipment and acquisition methods, the resolution, size, contrast, and clarity of images from different datasets show significant differences, resulting in poor generalization performance of deep learning networks. To solve this problem, this study proposes a multi-level segmentation network. The network includes data quality enhancement module (DQEM), coarse segmentation module (CSM), localization module (OLM), and fine segmentation stage module (FSM). In FSM, W-Net is proposed for the first time, and boundary loss is introduced in the loss function, which effectively improves the performance of OD segmentation. We generalized the model in the REFUGE test dataset, GAMMA dataset, Drishti-GS1 dataset, and IDRiD dataset, respectively. The results show that our method has the best OD segmentation performance in different datasets compared with state-of-the-art networks.

Response Fusion Attention U-ConvNext for accurate segmentation of optic disc and optic cup

Glaucoma is a pathological eye condition which requires accurate optic disc and optic cup segmentation for diagnosis. This study proposes Responsive Fusion Attention U-ConvNext, a novel encoder decoder network, for semantic segmentation of optic disc and optic cup structures from fundus images. Response Fusion Attention U-ConvNext is a U-Net like model containing a pre-trained ConvNext encoder network and a light-weight, modified ConvNext decoder network having skip connections between the encoder and decoder blocks. We also propose a new attention gate module called Dual-Path Response Fusion Attention (DPRFA) for smoothing the concatenation process of the encoder and upsampled feature maps. In addition, we propose a modified loss function by combining cross entropy, Dice and Jaccard losses for training the model accurately. The model has four sizes and all of them are trained and validated on DRISHTI-GS and REFUGE datasets. Our proposed models have acheived a dice coefficient of 0.9822 and 0.9269 on optic disc and optic cup segmentation of DRISHTI-GS dataset and a dice coefficient of 0.9788 and 0.9086 on optic disc and optic cup segmentation of REFUGE dataset. The experimental results thus obtained by our suggested models have shown state-of-the-art results when compared to other existing models. Code and models are available at : https://github.com/SiddMallick/RFAUCNxt-official.

Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images.

The accurate segmentation of the optic disc (OD) in fundus images is a crucial step for the analysis of many retinal diseases. However, because of problems such as vascular occlusion, parapapillary atrophy (PPA), and low contrast, accurate OD segmentation is still a challenging task. Therefore, this paper proposes a multiple preprocessing hybrid level set model (HLSM) based on area and shape for OD segmentation. The area-based term represents the difference of average pixel values between the inside and outside of a contour, while the shape-based term measures the distance between a prior shape model and the contour. The average intersection over union (IoU) of the proposed method was 0.9275, and the average four-side evaluation (FSE) was 4.6426 on a public dataset with narrow-angle fundus images. The IoU was 0.8179 and the average FSE was 3.5946 on a wide-angle fundus image dataset compiled from a hospital. The results indicate that the proposed multiple preprocessing HLSM is effective in OD segmentation.

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.

An Efficient Hierarchical Optic Disc and Cup Segmentation Network Combined with Multi-task Learning and Adversarial Learning.

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.

GDCSeg-Net: general optic disc and cup segmentation network for multi-device fundus images.

Accurate segmentation of optic disc (OD) and optic cup (OC) in fundus images is crucial for the analysis of many retinal diseases, such as the screening and diagnosis of glaucoma and atrophy segmentation. Due to domain shift between different datasets caused by different acquisition devices and modes and inadequate training caused by small sample dataset, the existing deep-learning-based OD and OC segmentation networks have poor generalization ability for different fundus image datasets. In this paper, adopting the mixed training strategy based on different datasets for the first time, we propose an encoder-decoder based general OD and OC segmentation network (named as GDCSeg-Net) with the newly designed multi-scale weight-shared attention (MSA) module and densely connected depthwise separable convolution (DSC) module, to effectively overcome these two problems. Experimental results show that our proposed GDCSeg-Net is competitive with other state-of-the-art methods on five different public fundus image datasets, including REFUGE, MESSIDOR, RIM-ONE-R3, Drishti-GS and IDRiD.

DRNet: Segmentation and localization of optic disc and Fovea from diabetic retinopathy image

Background and objectiveIn modern ophthalmology, automated Computer-aided Screening Tools (CSTs) are crucial non-intrusive diagnosis methods, where an accurate segmentation of Optic Disc (OD) and localization of OD and Fovea centers are substantial integral parts. However, designing such an automated tool remains challenging due to small dataset sizes, inconsistency in spatial, texture, and shape information of the OD and Fovea, and the presence of different artifacts. MethodsThis article proposes an end-to-end encoder-decoder network, named DRNet, for the segmentation and localization of OD and Fovea centers. In our DRNet, we propose a skip connection, named residual skip connection, for compensating the spatial information lost due to pooling in the encoder. Unlike the earlier skip connection in the UNet, the proposed skip connection does not directly concatenate low-level feature maps from the encoder's beginning layers with the corresponding same scale decoder. We validate DRNet using different publicly available datasets, such as IDRiD, RIMONE, DRISHTI-GS, and DRIVE for OD segmentation; IDRiD and HRF for OD center localization; and IDRiD for Fovea center localization. ResultsThe proposed DRNet, for OD segmentation, achieves mean Intersection over Union (mIoU) of 0.845, 0.901, 0.933, and 0.920 for IDRiD, RIMONE, DRISHTI-GS, and DRIVE, respectively. Our OD segmentation result, in terms of mIoU, outperforms the state-of-the-art results for IDRiD and DRIVE datasets, whereas it outperforms state-of-the-art results concerning mean sensitivity for RIMONE and DRISHTI-GS datasets. The DRNet localizes the OD center with mean Euclidean Distance (mED) of 20.23 and 13.34 pixels, respectively, for IDRiD and HRF datasets; it outperforms the state-of-the-art by 4.62 pixels for IDRiD dataset. The DRNet also successfully localizes the Fovea center with mED of 41.87 pixels for the IDRiD dataset, outperforming the state-of-the-art by 1.59 pixels for the same dataset. ConclusionAs the proposed DRNet exhibits excellent performance even with limited training data and without intermediate intervention, it can be employed to design a better-CST system to screen retinal images. Our source codes, trained models, and ground-truth heatmaps for OD and Fovea center localization will be made publicly available upon publication at GitHub.11https://github.com/kamruleee51/DRNet_Segmentation_Localization_OD_Fovea.

Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples.

Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. Such a design gives MSMKU a rich receptive field and is able to effectively represent multi-scale features. In addition, we designed a mixed maximum loss minimization learning strategy (MMLM) for training the proposed MSMKU. This training strategy can adaptively sort the samples by the loss function and re-weight the samples through data enhancement, thereby synchronously improving the prediction performance of all samples. Experiments show that the proposed method has obtained a state-of-the-art breakthrough result for OD and OC segmentation on the RIM-ONE-V3 and DRISHTI-GS datasets. At the same time, the proposed method achieved satisfactory glaucoma screening performance on the RIM-ONE-V3 and DRISHTI-GS datasets. On datasets with an imbalanced distribution between typical and rare sample images, the proposed method obtained a higher accuracy than existing deep learning methods.

Joint optic disc and cup segmentation using semi-supervised conditional GANs

Glaucoma is a chronic and widespread eye disease threatening humans’ irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important measurements used for glaucoma screening and diagnosis, requires accurate segmentation of optic disc and cup from fundus images. However, most existing techniques fail to obtain satisfactory segmentation performance because a significant number of pixel-level annotated data are often unavailable during training. To cope with this limitation, in this paper, we propose an effective joint optic disc and cup segmentation method based on semi-supervised conditional Generative Adversarial Nets (GANs). Our architecture consists of a segmentation net, a generator and a discriminator, to learn a mapping between the fundus images and the corresponding segmentation maps. Additionally, we employ both labeled and unlabeled data to improve the segmentation performance. The extensive experiments show that our method achieves state-of-the-art optic disc and cup segmentation results on both ORIGA and REFUGE datasets.

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.

Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central region which is the optic disc and the optic cup region within the disc are examined to determine one of the important cues for glaucoma diagnosis called the optic cup-to-disc ratio (CDR). CDR calculation requires accurate segmentation of optic disc and cup. Another important cue for glaucoma progression is the variation of depth in ONH region. In this paper, we first propose a deep learning framework to estimate depth from a single fundus image. For the case of monocular retinal depth estimation, we are also plagued by the labeled data insufficiency. To overcome this problem we adopt the technique of pretraining the deep network where, instead of using a denoising autoencoder, we propose a new pretraining scheme called pseudo-depth reconstruction, which serves as a proxy task for retinal depth estimation. Empirically, we show pseudo-depth reconstruction to be a better proxy task than denoising. Our results outperform the existing techniques for depth estimation on the INSPIRE dataset. To extend the use of depth map for optic disc and cup segmentation, we propose a novel fully convolutional guided network, where, along with the color fundus image the network uses the depth map as a guide. We propose a convolutional block called multimodal feature extraction block to extract and fuse the features of the color image and the guide image. We extensively evaluate the proposed segmentation scheme on three datasets- ORIGA, RIMONEr3, and DRISHTI-GS. The performance of the method is comparable and in many cases, outperforms the most recent state of the art.

Saliency‐Based Segmentation of Optic Disc in Retinal Images

Accurate segmentation of Optic disc (OD) is significant for the automation of retinal analysis and retinal diseases screening. This paper proposes a novel optic disc segmentation method based on the saliency. It includes two stages: optic disc location and saliency-based segmentation. In the location stage, the OD is detected using a matched template and the density of the vessels. In the segmentation stage, we treat the OD as the salient object and formulate it as a saliency detection problem. To measure the saliency of a region, the boundary prior and the connectivity prior are exploited. Geodesic distance to the window boundary is computed to measure the cost the region spends to reach the window boundary. After a threshold and ellipse fitting, we obtain the OD. Experimental results on two public databases for OD segmentation show that the proposed method achieves thestate-of-the-art performance.

Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two‐Layer Level Set Method

Glaucoma is a group of eye conditions, which can seriously damage optic nerves because of an elevated intraocular pressure. Nowadays, glaucoma has become one of the principal causes of blindness that results in irreversible visual loss. Early screening and treatment of glaucoma can prevent further progression of optic nerve degeneration effectively. The vertical cup‐to‐disc ratio (CDR) is a commonly used measurement for the detection of glaucoma, and therefore accurate segmentation of optic disc (OD) and optic cup (OC) regions in retinal fundus images is of great significance. In this paper, we present a prior shape constraint‐based two‐layer level set method for OD and OC segmentation in fundus images. This method uses two different layers of one level set function to represent the OD and OC boundaries. In this method, the distance regularized term is designed to guarantee that the distance between the OD and OC varies smoothly. By introducing the prior shape constraint term energy, the final segmentation results of OD and OC are always in the shape of approximate ellipses. In addition, the proposed method has the property of dealing with the intensity inhomogeneity of fundus images through the local fitting energy embedded. Experiments on images from the Baidu Research database demonstrate that the proposed method has superior performance in terms of accuracy and effectiveness.

Optic Disc and Cup Segmentation Based on Deep Convolutional Generative Adversarial Networks

Glaucoma is a chronic eye disease that causes loss of vision and it is irreversible. Accurate segmentation of optic disc and optic cup is a basic step in screening glaucoma. The most existing deep convolutional neural network (DCNN) methods have insufficient feature information extraction, and hence they are susceptible to pathological regions and low-quality images, with have poor ability to restore context information. Finally, the accuracy of the model segmentation is low. In this paper, we propose GL-Net, a multi-label DCNN model that combines the generative adversarial networks. GL-Net consists of two network structures including a Generator and a Discriminator. In the Generator, we use skip connection to promote the fusion of low-level feature information and high-level feature information, which alleviates the difficulty of restoring detailed feature information during upsampling, and reduces the downsampling factor, effectively alleviating excessive feature information loss. In the loss function, we add the $L_{1}$ distance function and the cross-entropy function to prevent the mode collapse when the model is trained, which makes the segmentation result more accurate. We use transfer learning and data augmentation to alleviate the problem of insufficient data and over-fitting of the model during training. Finally, GL-Net was verified on DRISHTI-GS1 dataset. The experimental results show that GL-Net outperforms some state-of-the-art method, such as M-Net, Stack-U-Net, RACE-net, and BCRF in terms of $F1$ and boundary distance localization error (BLE). Particularly, in the optic cup segmentation, GL-Net outperforms RACE-net by 3.5% and 4.16 pixels in terms of $F1$ and BLE, respectively.

An Accurate Optic Disc Segmentation from Peripapillary Atrophy Incident Retinal Images

Glaucoma detection system analyses the retinal images. This system gives vital information about presence of Glaucoma, the second largest cause of blindness. Usually the Glaucoma patients have large Cup-to-Disc ratio (CDR), inspection of CDR of Optic Disc is crucial of any Glaucoma detection system. Therefore accurate segmentation Optic Disc (OD) and Cup are critical in the formulation of CDR. Current work focuses on OD segmentation to develop an efficient Glaucoma detection system. A serious difficulty arises in OD segmentation in retinal images having inhomogenities due to Peripapillary Atrophy (PPA) and fuzzy boundaries. So this paper proposes a OD Segmentation method that takes care of fuzzy boundaries and inhomogenity presenting in the retinal images. At First, The matched filter with Cauchy kernel is applied to extract blood vessels. Then Vessel Directional Matched filter(VDM) is used to locate the centre of the optic disc approximately followed by Vessel Inpainting to erase the vasculature in the OD region. Finally a LBF energy based active contour model is formulated that embeds edge and region based information in the newly formulated locally computed signed pressure force(SPF) function , to segment OD. The obtained results indicate that the proposed OD segmentation outperforms many existing methods.