Cup Segmentation Research Articles

FDT‐Net: Frequency‐Aware Dual‐Branch Transformer‐Based Optic Cup and Optic Disk Segmentation With Parallel Contour Information Mining and Uncertainty‐Guided Refinement

ABSTRACTAccurate segmentation of the optic cup and disc in fundus images is crucial for the prevention and diagnosis of glaucoma. However, challenges arise due to factors such as blood vessels, and mainstream networks often demonstrate limited capacity in extracting contour information. In this paper, we propose a segmentation framework named FDT‐Net, which is based on a frequency‐aware dual‐branch Transformer (FDBT) architecture with parallel contour information mining and uncertainty‐guided refinement. Specifically, we design a FDBT that operates in the frequency domain. This module leverages the inherent contextual awareness of Transformers and utilizes Discrete Cosine Transform (DCT) transformation to mitigate the impact of certain interference factors on segmentation. The FDBT comprises global and local branches that independently extract global and local information, thereby enhancing segmentation results. Moreover, to further mine additional contour information, this study develops the parallel contour information mining (PCIM) module to operate in parallel. These modules effectively capture more details from the edges of the optic cup and disc while avoiding mutual interference, thus optimizing segmentation performance in contour regions. Furthermore, we propose an uncertainty‐guided refinement (UGR) module, which generates and quantifies uncertainty mass and leverages it to enhance model performance based on subjective logic theory. The experimental results on two publicly available datasets demonstrate the superior performance and competitive advantages of our proposed FDT‐Net. The code for this project is available at https://github.com/Rookie49144/FDT‐Net.

Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network.

Glaucoma represents a significant global contributor to blindness. Accurately segmenting the optic disc (OD) and optic cup (OC) to obtain precise CDR is essential for effective screening. However, existing convolutional neural network (CNN)-based segmentation techniques are often limited by high computational demands and long inference times. This paper proposes an efficient end-to-end method for OD and OC segmentation, utilizing the lightweight MobileNetv3 network as the core feature-extraction module. Our approach combines boundary branches with adversarial learning, to achieve multi-label segmentation of the OD and OC. We validated our proposed approach across three public available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The outcomes reveal that the Dice coefficients for the segmentation of OD and OC within these datasets are 0.974/0.900, 0.966/0.875, and 0.962/0.880, respectively. Additionally, our method substantially lowers computational complexity and inference time, thereby enabling efficient and precise segmentation of the optic disc and optic cup.

TriLA: Triple-Level Alignment Based Unsupervised Domain Adaptation for Joint Segmentation of Optic Disc and Optic Cup.

Cross-domain joint segmentation of optic disc and optic cup on fundus images is essential, yet challenging, for effective glaucoma screening. Although many unsupervised domain adaptation (UDA) methods have been proposed, these methods can hardly achieve complete domain alignment, leading to suboptimal performance. In this paper, we propose a triple-level alignment (TriLA) model to address this issue by aligning the source and target domains at the input level, feature level, and output level simultaneously. At the input level, a learnable Fourier domain adaptation (LFDA) module is developed to learn the cut-off frequency adaptively for frequency-domain translation. At the feature level, we disentangle the style and content features and align them in the corresponding feature spaces using consistency constraints. At the output level, we design a segmentation consistency constraint to emphasize the segmentation consistency across domains. The proposed model is trained on the RIGA+ dataset and widely evaluated on six different UDA scenarios. Our comprehensive results not only demonstrate that the proposed TriLA substantially outperforms other state-of-the-art UDA methods in joint segmentation of optic disc and optic cup, but also suggest the effectiveness of the triple-level alignment strategy.

Methodological Approaches to Optical Disc and Optical Cup Segmentation: A Critical Assessment

A progressive optic nerve condition called glaucoma causes irreversible eyesight loss. To diagnose retinal diseases, retinal fundus imaging has been used in recent years. Analyzing these images effectively requires pinpointing the areas of interest, which can be tricky, due to the anatomy and vascular patterns in fundus images. Different image segmentation techniques are used to extract the area of interest from the fundus images. This paper explores the various segmentation methodologies, emphasizing conventional and modern retinal fundus image segmentation approaches. Evaluation measures such as the Disc damage likelihood scale, Inferior superior temporal region, Dice similarity coefficient, Jaccard index, Sensitivity and Specificity are used to measure the effectiveness of segmentation algorithms, which detect small structural changes that indicate glaucomatous damage. Furthermore, this paper also provides a detailed analysis of deep learning algorithms used for optic cup and optic disc segmentation. This detailed analysis demonstrates that accurate disc and cup segmentation remains a significant challenge and suggests that effective segmentation strategies and deep learning approaches are required for vast and complex datasets.

Multi-dimensional dense attention network for pixel-wise segmentation of optic disc in colour fundus images.

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. Accurate segmentation of OD remains challenging due to blurred boundaries, vessel occlusion, and other distractions and limitations. These days, deep learning is rapidly progressing in the segmentation of image pixels, and a number of network models have been proposed for end-to-end image segmentation. However, there are still certain limitations, such as limited ability to represent context, inadequate feature processing, limited receptive field, etc., which lead to the loss of local details and blurred boundaries. A multi-dimensional dense attention network, or MDDA-Net, is proposed for pixel-wise segmentation of OD in retinal images in order to address the aforementioned issues and produce more thorough and accurate segmentation results. In order to acquire powerful contexts when faced with limited context representation capabilities, a dense attention block is recommended. A triple-attention (TA) block is introduced in order to better extract the relationship between pixels and obtain more comprehensive information, with the goal of addressing the insufficient feature processing. In the meantime, a multi-scale context fusion (MCF) is suggested for acquiring the multi-scale contexts through context improvement. Specifically, we provide a thorough assessment of the suggested approach on three difficult datasets. In the MESSIDOR and ORIGA data sets, the suggested MDDA-NET approach obtains accuracy levels of 99.28% and 98.95%, respectively. The experimental results show that the MDDA-Net can obtain better performance than state-of-the-art deep learning models under the same environmental conditions.

LC-MANet: Location-constrained joint optic disc and cup segmentation via multiplex aggregation network

Many early ophthalmology-related diseases can be detected through fundus images. The cup-to-disc ratio (CDR) is an important criterion for the early screening and diagnosis of glaucoma. However, accurately calculating it is a challenging task primarily due to blurred boundaries and the physiological structure of the fundus. To alleviate these issues, this paper designs a location-constrained joint optic disc (OD) and optic cup (OC) segmentation method using the multiplex aggregation network (LC-MANet). This method incorporates independent segmentation and joint segmentation within a coarse-to-fine framework. In the independent segmentation stage, this paper utilizes the initial OD and OC as prior location auto-context information to constrain the joint segmentation of OD and OC. The proposed multiplex aggregation network combines features from the corresponding layer and across layers to capture global and local information. This paper employs a multi-channel fusion strategy in image preprocessing to suppress vascular interference in OD independent segmentation. To emphasize the superiority of our method, it is important to highlight the significant number of test images used in the evaluation. Our experiments are conducted on three public datasets, namely RIM-ONE (60 images), Drishti-GS (51 images), and REFUGE (400 images). Experimental results demonstrate that our method outperforms most others in terms of Dice Coefficient and absolute error. The accuracy of glaucoma screening achieved by our proposed method for the Drishti-GS, REFUGE, and RIM-ONE-r3 datasets is 0.9216, 0.9600, and 0.9000, respectively. The proposed framework successfully designs a multiplex aggregation network for a coarse-to-fine joint segmentation model and achieves state-of-the-art performance in OD and OC segmentation. Furthermore, it can be applied to other medical image segmentation tasks.

Automated analysis for glaucoma screening of retinal videos acquired with smartphone-based ophthalmoscope

Widespread screening is crucial for the early diagnosis and treatment of glaucoma, the leading cause of visual impairment and blindness. The development of portable technologies, such as smartphone-based ophthalmoscopes, able to image the optical nerve head, represents a resource for large-scale glaucoma screening. Indeed, they consist of an optical device attached to a common smartphone, making the overall device cheap and easy to use. Automated analyses able to assist clinicians are crucial for fast, reproducible, and accurate screening, and can promote its diffusion making it possible even for non-expert ophthalmologists. Images acquired with smartphone ophthalmoscopes differ from that acquired with a fundus camera for the field of view, noise, colour, and the presence of pupil, iris and eyelid. Consequently, algorithms specifically designed for this type of image need to be developed.We propose a completely automated analysis of retinal video acquired with smartphone ophthalmoscopy. The proposed algorithm, based on convolutional neural networks, selects the most relevant frames in the video, segments both optic disc and cup, and computes the cup-to-disc ratio. The developed networks were partially trained on images from a publicly available fundus camera datasets, modified through an original procedure to be statistically equal to the ones acquired with a smartphone ophthalmoscope. The proposed algorithm achieves good results in images acquired from healthy and pathological subjects. Indeed, an accuracy ≥95 % was obtained for both disc and cup segmentation and the computed cup-to-disc ratios denote good agreement with manual analysis (mean difference 9 %), allowing a substantial differentiation between healthy and pathological subjects.

GREnet: Gradually REcurrent Network With Curriculum Learning for 2-D Medical Image Segmentation.

Medical image segmentation is a vital stage in medical image analysis. Numerous deep-learning methods are booming to improve the performance of 2-D medical image segmentation, owing to the fast growth of the convolutional neural network. Generally, the manually defined ground truth is utilized directly to supervise models in the training phase. However, direct supervision of the ground truth often results in ambiguity and distractors as complex challenges appear simultaneously. To alleviate this issue, we propose a gradually recurrent network with curriculum learning, which is supervised by gradual information of the ground truth. The whole model is composed of two independent networks. One is the segmentation network denoted as GREnet, which formulates 2-D medical image segmentation as a temporal task supervised by pixel-level gradual curricula in the training phase. The other is a curriculum-mining network. To a certain degree, the curriculum-mining network provides curricula with an increasing difficulty in the ground truth of the training set by progressively uncovering hard-to-segmentation pixels via a data-driven manner. Given that segmentation is a pixel-level dense-prediction challenge, to the best of our knowledge, this is the first work to function 2-D medical image segmentation as a temporal task with pixel-level curriculum learning. In GREnet, the naive UNet is adopted as the backbone, while ConvLSTM is used to establish the temporal link between gradual curricula. In the curriculum-mining network, UNet++ supplemented by transformer is designed to deliver curricula through the outputs of the modified UNet++ at different layers. Experimental results have demonstrated the effectiveness of GREnet on seven datasets, i.e., three lesion segmentation datasets in dermoscopic images, an optic disc and cup segmentation dataset and a blood vessel segmentation dataset in retinal images, a breast lesion segmentation dataset in ultrasound images, and a lung segmentation dataset in computed tomography (CT).

Entropy and distance-guided super self-ensembling for optic disc and cup segmentation.

Segmenting the optic disc (OD) and optic cup (OC) is crucial to accurately detect changes in glaucoma progression in the elderly. Recently, various convolutional neural networks have emerged to deal with OD and OC segmentation. Due to the domain shift problem, achieving high-accuracy segmentation of OD and OC from different domain datasets remains highly challenging. Unsupervised domain adaptation has taken extensive focus as a way to address this problem. In this work, we propose a novel unsupervised domain adaptation method, called entropy and distance-guided super self-ensembling (EDSS), to enhance the segmentation performance of OD and OC. EDSS is comprised of two self-ensembling models, and the Gaussian noise is added to the weights of the whole network. Firstly, we design a super self-ensembling (SSE) framework, which can combine two self-ensembling to learn more discriminative information about images. Secondly, we propose a novel exponential moving average with Gaussian noise (G-EMA) to enhance the robustness of the self-ensembling framework. Thirdly, we propose an effective multi-information fusion strategy (MFS) to guide and improve the domain adaptation process. We evaluate the proposed EDSS on two public fundus image datasets RIGA+ and REFUGE. Large amounts of experimental results demonstrate that the proposed EDSS outperforms state-of-the-art segmentation methods with unsupervised domain adaptation, e.g., the Dicemean score on three test sub-datasets of RIGA+ are 0.8442, 0.8772 and 0.9006, respectively, and the Dicemean score on the REFUGE dataset is 0.9154.

Glaucoma Detection using Fundus Images by Extracting Localized Disc Features

Glaucoma is one of the leading causes of blindness worldwide. It occurs due to high pressure in the eyes and other factors such as family history, age, ethnicity, etc. It damages the optic nervous system which is irreversible damage. That's why regular screening for glaucoma is crucial and recommended. Researchers are continuously searching for better methods to identify glaucoma at early stages before it becomes worse and incurable. Significant work has been conducted on it, but there is still room for improvement. The main goal of this study is to propose a reliable system for glaucoma detection that considers the key factors contributing to glaucoma development, in accordance with the decisions made by clinical experts. In this work, the U-net model is used with EfficentNetb3 as a backbone model for optic disc and optical cup segmentation. In addition, a general deep learning model that has 1D convolution layers and other basic layers is used for glaucoma classification. Features extracted from optical disc and optical cup are used to train the deep learning model and overall 95% classification accuracy and 0.92 AUC are achieved for glaucoma classification on the RIGA dataset.

A review of optic disc and optic cup segmentation based on fundus images

AbstractOptic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic medicine and is crucial for aiding glaucoma screening. With the development of smart healthcare and the increase of large datasets, there is an increasing number of research efforts targeting OD and OC segmentation, making it particularly important to provide a systematic review of the latest advances in the field. This paper presents a systematic review of commonly used datasets, evaluation metrics, and related research results in the field of OD and OC segmentation. The advantages and disadvantages of segmentation techniques based on traditional and deep learning methods are comparatively analysed. In addition, this study emphasizes the importance of OD and OC segmentation efforts in smart healthcare. Despite the technological advances, the lack of generalization capability is still a major obstacle limiting its clinical application. To address this issue, this study explores unsupervised domain adaptation methods to enhance the generalization performance of segmentation techniques and provide new strategies for clinical diagnosis. Finally, this paper discusses the challenges and future research directions faced by OD and OC segmentation when applied in the medical field to help readers comprehensively grasp the research dynamics in this area.

Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment

Automated segmentation is a challenging task in medical image analysis that usually requires a large amount of manually labeled data. However, most current supervised learning based algorithms suffer from insufficient manual annotations, posing a significant difficulty for accurate and robust segmentation. In addition, most current semi-supervised methods lack explicit representations of geometric structure and semantic information, restricting segmentation accuracy. In this work, we propose a hybrid framework to learn polygon vertices, region masks, and their boundaries in a weakly/semi-supervised manner that significantly advances geometric and semantic representations. Firstly, we propose multi-granularity learning of explicit geometric structure constraints via polygon vertices (PolyV) and pixel-wise region (PixelR) segmentation masks in a semi-supervised manner. Secondly, we propose eliminating boundary ambiguity by using an explicit contrastive objective to learn a discriminative feature space of boundary contours at the pixel level with limited annotations. Thirdly, we exploit the task-specific clinical domain knowledge to differentiate the clinical function assessment end-to-end. The ground truth of clinical function assessment, on the other hand, can serve as auxiliary weak supervision for PolyV and PixelR learning. We evaluate the proposed framework on two tasks, including optic disc (OD) and cup (OC) segmentation along with vertical cup-to-disc ratio (vCDR) estimation in fundus images; left ventricle (LV) segmentation at end-diastolic and end-systolic frames along with ejection fraction (LVEF) estimation in two-dimensional echocardiography images. Experiments on nine large-scale datasets of the two tasks under different label settings demonstrate our model’s superior performance on segmentation and clinical function assessment.

Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism.

Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural network model for optic disc and cup disc segmentation. The U-Net backbone is used to infer the basic position information of optic disc and cup disc, the attention module enhances the model's ability to represent and extract features of optic disc and cup disc, and the residual module alleviates gradient disappearance or explosion that may occur during feature representation of the neural network. The proposed model is trained and tested on the DRISHTI-GS1 dataset. Results show that compared with the original U-Net method, our model can more effectively separate optic disc and cup disc in terms of overlap error, sensitivity, and specificity.

Local–global pseudo-label correction for source-free domain adaptive medical image segmentation

Domain shift is a commonly encountered issue in medical imaging solutions, primarily caused by variations in imaging devices and data sources. To mitigate this problem, unsupervised domain adaptation techniques have been employed. However, concerns regarding patient privacy and potential degradation of image quality have led to an increased focus on source-free domain adaptation. In this study, we address the issue of false labels in self-training based source-free domain adaptive medical image segmentation methods. To correct erroneous pseudo-labels, we propose a novel approach called the local–global pseudo-label correction (LGDA) method for source-free domain adaptive medical image segmentation. Our method consists of two components: An offline local context-based pseudo-label correction method that utilizes local context similarity in image space. And an online global pseudo-label correction method based on class prototypes, which corrects erroneously predicted pseudo-labels by considering the relative distance between pixel-wise feature vectors and prototype vectors. We evaluate the performance of our method on three benchmark fundus image datasets for optic disc and cup segmentation. Our method achieves superior performance compared to the state-of-the-art approaches, even without using any source data.

Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network

Image segmentation is a fundamental task in deep learning, which is able to analyse the essence of the images for further development. However, for the supervised learning segmentation method, collecting pixel-level labels is very time-consuming and labour-intensive. In the medical image processing area for optic disc and cup segmentation, we consider there are two challenging problems that remain unsolved. One is how to design an efficient network to capture the global field of the medical image and execute fast in real applications. The other is how to train the deep segmentation network using a few training data due to some medical privacy issues. In this paper, to conquer such issues, we first design a novel attention-aware segmentation model equipped with the multi-scale attention module in the pyramid structure-like encoder-decoder network, which can efficiently learn the global semantics and the long-range dependencies of the input images. Furthermore, we also inject the prior knowledge that the optic cup lies inside the optic disc by a novel loss function. Then, we propose a self-supervised contrastive learning method for optic disc and cup segmentation. The unsupervised feature representation is learned by matching an encoded query to a dictionary of encoded keys using a contrastive technique. Finetuning the pre-trained model using the proposed loss function can help achieve good performance for the task. To validate the effectiveness of the proposed method, extensive systemic evaluations on different public challenging optic disc and cup benchmarks, including DRISHTI-GS and REFUGE datasets demonstrate the superiority of the proposed method, which can achieve new state-of-the-art performance approaching 0.9801 and 0.9087 F1 score respectively while gaining 0.9657 DCdisc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$DC_{disc}$$\\end{document} and 0.8976 DCcup\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$DC_{cup}$$\\end{document}. The code will be made publicly available.

Novel Methods for Diagnosing Glaucoma: Segmenting Optic Discs and Cups using Ensemble Learning Algorithms and CDR Ratio Analysis

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.

Vision transformers for segmentation of disc and cup in retinal fundus images

Several semantic segmentation models have been proposed in recent years to screen for Glaucoma in retinal fundus images, but the lack of comprehensive benchmark datasets makes it difficult to analyze their generalization ability. This paper conveys two main contributions. In the first contribution, it proposes a transformer-based model with two segmentation heads for detecting cup-and-disc in retinal fundus images. The model has two different architectures based on the mon- olithic vanilla vision transformer (ViT) and the multiscale Swin transformer, and is trained on both original images and their cropped version over the optical disc. During inference, the test image is fed into the first head for initial cropping, and then the resulting cropped image is fed into the second head for generating a refined segmentation map. The architecture is learnable end-to-end and does not require any pre- or post-processing tasks, providing promising results compared to state-of-the- art methods. In the second contribution, the models are evaluated on a new benchmark problem consisting of eight retinal datasets using a leave-one-out evaluation protocol to assess the generali- zation capability of the models. The monolithic ViT-based model yields average dice scores of 93.55% and 85.33% for the cup and disc, respectively, and a cup-to-disc ratio of 0.054 over eight scenarios, while the hierarchical Swin-based model provides average dice scores of 94.45% and 85.31% for the cup and disc, respectively, and a cup-to-disc ratio of 0.055. The method demo is available at: https://huggingface.co/spaces/BigData-KSU/Glaucoma-Detection.

Deep convolutional neural network for glaucoma detection based on image classification

PURPOSE: Many researchers have found that the improvement in computerised medical imaging has pushed them to their limits in terms of developing automated algorithms for the identification of illness without the need for human participation. The diagnosis of glaucoma, among other eye illnesses, has continued to be one of the most difficult tasks in the area of medicine. Because there are not enough skilled specialists and there are a lot of patients seeking treatment from ophthalmologists, we have been encouraged to build efficient computer-based diagnostic methods that can assist medical professionals in early diagnosis and help reduce the amount of time and effort they spend working on healthy situations. The Optic Disc position is determined with the help of the LoG operator, and a Disc Image map is projected with the help of a U-net architecture by utilising the location and intensity profile of the optic disc. After this, a Generative adversarial network is suggested as a possible solution for segmenting the disc border. In order to verify the performance of the model, a well-defined investigation is carried out on many retinal datasets. The usage of a multi-encoder U-net framework for optic cup segmentation is the second key addition made by this proposed work. This framework greatly outperforms the state-of-the-art in this area. The suggested algorithms have been tested on public standard datasets such as Drishti-GS, Origa, and Refugee, as well as a private community camp-based difficult dataset obtained from the All-India Institute of Medical Sciences (AIIMS), Delhi. All of these datasets have been verified. In conclusion, we have shown some positive outcomes for the detection of diseases. The unique strategy for glaucoma treatment is called ensemble learning, and it combines clinically meaningful characteristics with a deep Convolutional Neural Network.

Glaucoma Identification Using Convolutional Neural Networks Ensemble for Optic Disc and Cup Segmentation

Glaucoma Identification Using Convolutional Neural Networks Ensemble for Optic Disc and Cup Segmentation

Combined Optic Disc and Optic Cup Segmentation Network Based on Adversarial Learning

Combined Optic Disc and Optic Cup Segmentation Network Based on Adversarial Learning