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Artykuły w czasopismach na temat „Optic cup segmentation”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Zhou, Wei, Yugen Yi, Yuan Gao, and Jiangyan Dai. "Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior." Computational and Mathematical Methods in Medicine 2019 (November 20, 2019): 1–16. http://dx.doi.org/10.1155/2019/8973287.

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Accurate optic disc and optic cup segmentation plays an important role for diagnosing glaucoma. However, most existing segmentation approaches suffer from the following limitations. On the one hand, image devices or illumination variations always lead to intensity inhomogeneity in the fundus image. On the other hand, the spatial prior knowledge of optic disc and optic cup, e.g., the optic cup is always contained inside the optic disc region, is ignored. Therefore, the effectiveness of segmentation approaches is greatly reduced. Different from most previous approaches, we present a novel locall

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Rakes, Geethalakshmi, and Vani Rajamanickam. "A Novel Deep Learning Algorithm for Optical Disc Segmentation for Glaucoma Diagnosis." Traitement du Signal 39, no. 1 (2022): 305–11. http://dx.doi.org/10.18280/ts.390132.

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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

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Jiang, Yun, Falin Wang, Jing Gao, and Simin Cao. "Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image." Applied Sciences 10, no. 11 (2020): 3777. http://dx.doi.org/10.3390/app10113777.

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Diabetes can induce diseases including diabetic retinopathy, cataracts, glaucoma, etc. The blindness caused by these diseases is irreversible. Early analysis of retinal fundus images, including optic disc and optic cup detection and retinal blood vessel segmentation, can effectively identify these diseases. The existing methods lack sufficient discrimination power for the fundus image and are easily affected by pathological regions. This paper proposes a novel multi-path recurrent U-Net architecture to achieve the segmentation of retinal fundus images. The effectiveness of the proposed network

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4

Alia Zainudin, Noraina, Ain Nazari, Mohd Marzuki Mustafa, Wan NurShazwani Wan Zakaria, Nor Surayahani Suriani, and Wan Nur Hafsha Wan Kairuddin. "Glaucoma detection of retinal images based on boundary segmentation." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (2020): 377. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp377-384.

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<p>The rapid growth of technology makes it possible to implement in immediate diagnosis for patients using image processing. By using morphological processing and adaptive thresholding method for segmentation of optic disc and optic cup, various sizes of retinal fundus images captured through fundus camera from online databases can be processed. This paper explains the use of color channel separation method for pre-processing to remove noise for better optic disc and optic cup segmentation. Noise removal will improve image quality and in return help to increase segmentation standard. The

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Noraina, Alia Zainudin, Nazari Ain, Marzuki Mustafa Mohd, NurShazwani Wan Zakaria Wan, Surayahani Suriani Nor, and Nur Hafsha Wan Kairuddin Wan. "Glaucoma detection of retinal images based on boundary segmentation." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 1 (2020): 377–84. https://doi.org/10.11591/ijeecs.v18.i1.pp377-384.

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The rapid growth of technology makes it possible to implement in immediate diagnosis for patients using image processing. By using morphological processing and adaptive thresholding method for segmentation of optic disc and optic cup, various sizes of retinal fundus images captured through fundus camera from online databases can be processed. This paper explains the use of color channel separation method for pre-processing to remove noise for better optic disc and optic cup segmentation. Noise removal will improve image quality and in return help to increase segmentation standard. Then, morpho

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6

Zhang, Fengming, Shuiwang Li, and Jianzhi Deng. "Unsupervised Domain Adaptation with Shape Constraint and Triple Attention for Joint Optic Disc and Cup Segmentation." Sensors 22, no. 22 (2022): 8748. http://dx.doi.org/10.3390/s22228748.

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Currently, glaucoma has become an important cause of blindness. At present, although glaucoma cannot be cured, early treatment can prevent it from getting worse. A reliable way to detect glaucoma is to segment the optic disc and cup and then measure the cup-to-disc ratio (CDR). Many deep neural network models have been developed to autonomously segment the optic disc and the optic cup to help in diagnosis. However, their performance degrades when subjected to domain shift. While many domain-adaptation methods have been exploited to address this problem, they are apt to produce malformed segmen

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7

Adi Nugroho, Hanung, Thea Kirana, Vicko Pranowo, and Augustine Herini Tita Hutami. "Optic cup segmentation using adaptive threshold and morphological image processing." Communications in Science and Technology 4, no. 2 (2019): 63–67. http://dx.doi.org/10.21924/cst.4.2.2019.125.

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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-

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8

Almazroa, Ahmed, Ritambhar Burman, Kaamran Raahemifar, and Vasudevan Lakshminarayanan. "Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey." Journal of Ophthalmology 2015 (2015): 1–28. http://dx.doi.org/10.1155/2015/180972.

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Glaucoma is the second leading cause of loss of vision in the world. Examining the head of optic nerve (cup-to-disc ratio) is very important for diagnosing glaucoma and for patient monitoring after diagnosis. Images of optic disc and optic cup are acquired by fundus camera as well as Optical Coherence Tomography. The optic disc and optic cup segmentation techniques are used to isolate the relevant parts of the retinal image and to calculate the cup-to-disc ratio. The main objective of this paper is to review segmentation methodologies and techniques for the disc and cup boundaries which are ut

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9

Mangipudi, Partha Sarathi, Hari Mohan Pandey, and Ankur Choudhary. "Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture." Multimedia Tools and Applications 80, no. 20 (2021): 30143–63. http://dx.doi.org/10.1007/s11042-020-10430-6.

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AbstractGlaucoma 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

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10

Tadisetty, Srikanth, Ranjith Chodavarapu, Ruoming Jin, Robert J. Clements, and Minzhong Yu. "Identifying the Edges of the Optic Cup and the Optic Disc in Glaucoma Patients by Segmentation." Sensors 23, no. 10 (2023): 4668. http://dx.doi.org/10.3390/s23104668.

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With recent advancements in artificial intelligence, fundus diseases can be classified automatically for early diagnosis, and this is an interest of many researchers. The study aims to detect the edges of the optic cup and the optic disc of fundus images taken from glaucoma patients, which has further applications in the analysis of the cup-to-disc ratio (CDR). We apply a modified U-Net model architecture on various fundus datasets and use segmentation metrics to evaluate the model. We apply edge detection and dilation to post-process the segmentation and better visualize the optic cup and opt

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Lina Jia. "Optic Cup and Optic Disc Segmentation Based on improved TransUnet." Journal of Electrical Systems 20, no. 7s (2024): 586–93. http://dx.doi.org/10.52783/jes.3362.

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Glaucoma ranks as the second leading cause of blindness globally, surpassed only by cataracts. It inflicts irreversible harm to the optic nerve, and once vision is lost, restoration is unattainable for life[1]. Therefore, early detection of glaucoma is imperative.The cup-to-disc ratio serves as a primary diagnostic tool for identifying glaucoma. Generally, an excessively large cup-to-disc ratio in fundus photographs strongly suggests glaucoma. However, human error in diagnosing fundus photographs by clinicians is prevalent, leading to time-consuming, labor-intensive, expensive, and potentially

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12

Dash, Sonali, P. Satish Rama Chowdary, V. V. S. S. S. Chakravarthy, et al. "Real Time Retinal Optic Disc Segmentation via Guided filter and Discrete Wavelet Transform." Journal of Physics: Conference Series 2312, no. 1 (2022): 012007. http://dx.doi.org/10.1088/1742-6596/2312/1/012007.

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Abstract In the world, glaucoma is the one of the main causes for loosing vision. Optical Coherence Tomography or fundus camera is utilized to capture the optic disc and optic cup images. For the detection of glaucoma and afterwards monitoring the patients, investigation of the head of optic nerve or cup-to-disc ratio (CDR) is an important factor. For computing the CDR value, segmentation of optic disc and optic cup are utilized for the isolation of the relevant parts of the fundus image. Even though ophthalmologists are computing the CDR value physically, however, it limits the identification

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13

Xiao, Zhang, Geng, Zhang, Wu, and Liu. "Research on the Method of Color Fundus Image Optic Cup Segmentation Based on Deep Learning." Symmetry 11, no. 7 (2019): 933. http://dx.doi.org/10.3390/sym11070933.

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The optic cup is a physiological structure in the fundus and is a small central depression in the eye. It has a normal proportion in the optic papilla. If the ratio is large, its size may be used to determine diseases such as glaucoma or congenital myopia. The occurrence of glaucoma is generally accompanied by physical changes to the optic cup, optic disc, and optic nerve fiber layer. Therefore, accurate measurement of the optic cup is important for the detection of glaucoma. The accurate segmentation of the optic cup is essential for the measurement of the size of the optic cup relative to ot

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14

Touahri, Radia, Nabiha Azizi, Nacer Eddine Hammami, Farid Benaida, Nawel Zemmal, and Ibtissem Gasmi. "An Improved Disc Segmentation Based on U-Net Architecture for Glaucoma Diagnosis." International Journal of Ambient Computing and Intelligence 13, no. 1 (2022): 1–18. http://dx.doi.org/10.4018/ijaci.313965.

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Various computer-aided diagnosis systems have been expanded and used for diagnosing glaucoma. Since the optic disc and optic cup are the main parameters for the early detection of glaucoma, this study proposes an accurate CAD system that firstly detects the optic disc and cup then classifies them into normal or abnormal. The U-Net architecture is employed. Despite its excellent segmentation performances, this model repeatedly extracts low-level features, which leads to redundant use of computational sources. To address these issues, a two-stage segmentation of the optic disc and cup was propos

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Naga Kiran D and Kanchana V. "Recognistion of glaucoma using otsu segmentation method." International Journal of Research in Pharmaceutical Sciences 10, no. 3 (2019): 1988–96. http://dx.doi.org/10.26452/ijrps.v10i3.1407.

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Glaucoma is an eye disease once it occurs, it cannot be cured. Be that as it may, in the event that it is starting stage doesn't take the therapeutic treatment its prompts the lasting visual impairment. Most of the literature surveys explain various techniques which are used with the help of the optic cup and optic disc to detect glaucoma. It tends to be successfully identified through the best possible segmentation of optic cup and optic disc. In this paper, we proposed about, NRR [neuro retinal rim] OTSU segmentation based technique. The disease will be confirmed by calculating the CDR [cup

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Al Sariera, Thamer Mitib, and Lalitha Rangarajan. "Optic disc and optic cup segmentation in retinal images." International Journal of Medical Engineering and Informatics 13, no. 2 (2021): 111. http://dx.doi.org/10.1504/ijmei.2021.10034765.

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Sariera, Thamer Mitib Al, and Lalitha Rangarajan. "Optic disc and optic cup segmentation in retinal images." International Journal of Medical Engineering and Informatics 13, no. 2 (2021): 111. http://dx.doi.org/10.1504/ijmei.2021.113391.

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N. Maldhure, Mr Prasad, and Prof V. V. Dixit. "Glaucoma Detection Using Optic Cup and Optic Disc Segmentation." International Journal of Engineering Trends and Technology 20, no. 2 (2015): 52–55. http://dx.doi.org/10.14445/22315381/ijett-v20p212.

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Chen, Yuanqiong, Zhijie Liu, Yujia Meng, and Jianfeng Li. "Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network." Biomimetics 9, no. 10 (2024): 637. http://dx.doi.org/10.3390/biomimetics9100637.

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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 s

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20

Prastyo, Pulung Hendro, Amin Siddiq Sumi, and Annis Nuraini. "Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image." JITCE (Journal of Information Technology and Computer Engineering) 4, no. 02 (2020): 105–9. http://dx.doi.org/10.25077/jitce.4.02.105-109.2020.

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Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross E

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Sun, Jinyang, Fangjun Luan, and Hanhui Wu. "Optic Disc Segmentation by Balloon Snake with Texture from Color Fundus Image." International Journal of Biomedical Imaging 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/528626.

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A well-established method for diagnosis of glaucoma is the examination of the optic nerve head based on fundus image as glaucomatous patients tend to have larger cup-to-disc ratios. The difficulty of optic segmentation is due to the fuzzy boundaries and peripapillary atrophy (PPA). In this paper a novel method for optic nerve head segmentation is proposed. It uses template matching to find the region of interest (ROI). The method of vessel erasing in the ROI is based on PDE inpainting which will make the boundary smoother. A novel optic disc segmentation approach using image texture is explore

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Liu, Zhijie, Yuanqiong Chen, Xiaohua Xiang, Zhan Li, Bolin Liao, and Jianfeng Li. "An End-to-End Real-Time Lightweight Network for the Joint Segmentation of Optic Disc and Optic Cup on Fundus Images." Mathematics 10, no. 22 (2022): 4288. http://dx.doi.org/10.3390/math10224288.

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Glaucoma is the second-most-blinding eye disease in the world and accurate segmentation of the optic disc (OD) and optic cup (OC) is essential for the diagnosis of glaucoma. To solve the problems of poor real-time performance, high algorithm complexity, and large memory consumption of fundus segmentation algorithms, a lightweight segmentation algorithm, GlauNet, based on convolutional neural networks, is proposed. The algorithm designs an efficient feature-extraction network and proposes a multiscale boundary fusion (MBF) module, which greatly improves the segmentation efficiency of the algori

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Božić-Štulić, Dunja, Maja Braović, and Darko Stipaničev. "Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images." International journal of electrical and computer engineering systems 11, no. 2 (2020): 111–20. http://dx.doi.org/10.32985/ijeces.11.2.6.

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Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and D

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Li, Ge, Changsheng Li, Chan Zeng, Peng Gao, and Guotong Xie. "Region Focus Network for Joint Optic Disc and Cup Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 751–58. http://dx.doi.org/10.1609/aaai.v34i01.5418.

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Glaucoma is one of the three leading causes of blindness in the world and is predicted to affect around 80 million people by 2020. The optic cup (OC) to optic disc (OD) ratio (CDR) in fundus images plays a pivotal role in the screening and diagnosis of glaucoma. Existing methods usually crop the optic disc region first, and subsequently perform segmentation in this region. However, these approaches come up with high complexities due to the separate operations. To remedy this issue, we propose a Region Focus Network (RF-Net) that innovatively integrates detection and multi-class segmentation in

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Sahu, Deepti, and Mandeep Kaur. "Methodological Approaches to Optical Disc and Optical Cup Segmentation: A Critical Assessment." International Journal of Experimental Research and Review 42 (August 30, 2024): 328–42. http://dx.doi.org/10.52756/ijerr.2024.v42.029.

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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 suc

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Aouf, Mohamed, Dalia Ali, and Ghada Kareem. "Optic Disc and Optic Cup Segmentation Methodology for Glaucoma Detection." International Journal of Engineering Research and Technology 13, no. 4 (2020): 706. http://dx.doi.org/10.37624/ijert/13.4.2020.706-714.

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Chen, Yuanyuan, Yongpeng Bai, and Yifan Zhang. "Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism." PeerJ Computer Science 10 (March 28, 2024): e1941. http://dx.doi.org/10.7717/peerj-cs.1941.

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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

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Prof. Vaishali Sarangpure. "CUP and DISC OPTIC Segmentation Using Optimized Superpixel Classification for Glaucoma Screening." International Journal of New Practices in Management and Engineering 3, no. 03 (2014): 07–11. http://dx.doi.org/10.17762/ijnpme.v3i03.30.

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Glaucoma, an incurable disease related to eyes which results in loss of the vision. Identifying this disease within in a proper period of time is most important, since it cannot be cured. The important aspect of this paper is to detect glaucoma at initial stages. Segmentation in the optic disc necessitates the differentiation of each super pixel by employing Histograms, centre surround statistics. Information location in merged with the above methods in increasing the performance of optic cup segmentation. Optic disc and optic cups are employed to evaluate cup to disc ratio of the disease iden

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Surendiran, J., S. Theetchenya, P. M. Benson Mansingh, et al. "Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network." BioMed Research International 2022 (May 2, 2022): 1–8. http://dx.doi.org/10.1155/2022/6799184.

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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 FC

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Song, Yantao, Wenjie Zhang, and Yue Zhang. "A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection." Mathematical Biosciences and Engineering 21, no. 4 (2024): 5092–117. http://dx.doi.org/10.3934/mbe.2024225.

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<abstract> <p>Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation of the OC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted in great success on this task. However, most existing methods either have unsatisfactory segme

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Manju, K., R. S.Sabeenian, and A. Surendar. "A Review on Optic Disc and Cup Segmentation." Biomedical and Pharmacology Journal 10, no. 1 (2017): 373–79. http://dx.doi.org/10.13005/bpj/1118.

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Sun, Yanxia, Peiqing, Xiaoxu Geng, Haiying Wang, Jinke Wang, and Shinichi Tamura. "Full Convolutional Neural Network with Multi-Scale Residual Model for Optic Cup and Disc Segmentation." Journal of Medical Imaging and Health Informatics 10, no. 11 (2020): 2733–38. http://dx.doi.org/10.1166/jmihi.2020.3208.

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Accurate optic cup and optic disc (OC, OD) segmentation is the prerequisite for cup-disc ratio (CDR) calculation. In this paper, a new full convolutional neural network (FCN) with multi-scale residual module is proposed. Firstly, polar coordinate transformation was introduced to balance the CDR with space constraints, and CLAHE was implemented in fundus images for contrast enhancement. Secondly, W-Net-R model was proposed as the main framework, while the standard convolution unit was replaced by the multi-scale residual module. Finally, the multi-label cost function is utilized to guide its fu

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Freire, Thiago Paiva, Geraldo Braz Júnior, João Dallyson Sousa de Almeida, and José Ribamar Durand Rodrigues Junior. "Cup and Disc Segmentation in Smartphone Handheld Ophthalmoscope Images with a Composite Backbone and Double Decoder Architecture." Vision 9, no. 2 (2025): 32. https://doi.org/10.3390/vision9020032.

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Glaucoma is a visual disease that affects millions of people, and early diagnosis can prevent total blindness. One way to diagnose the disease is through fundus image examination, which analyzes the optic disc and cup structures. However, screening programs in primary care are costly and unfeasible. Neural network models have been used to segment optic nerve structures, assisting physicians in this task and reducing fatigue. This work presents a methodology to enhance morphological biomarkers of the optic disc and cup in images obtained by a smartphone coupled to an ophthalmoscope through a de

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R. Geethalakshmi, Et al. "A Comparison of Deep Learning Techniques for Glaucoma Diagnosis on Retinal Fundus Images." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 3382–87. http://dx.doi.org/10.17762/ijritcc.v11i9.9545.

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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 detectio

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Jin, Zixiao. "Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation." ITM Web of Conferences 70 (2025): 03022. https://doi.org/10.1051/itmconf/20257003022.

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Glaucoma, the leading cause of irreversible blindness, must be diagnosed early and thus treated in time. However, it has no noticeable symptoms in its early stages and may not be detected easily. This paper aims to compare two well-known convolutional neural network (CNN) structures, namely Fully Convolutional Networks (FCNs) and U-Net for the segmentation of the optic disc (OD) and optic cup (OC) from retinal fundus images which play an important role in glaucoma diagnosis. The performance of both models is assessed using qualitative parameters such as the Dice coefficient, Jaccard index, and

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Jiang, Lincen, Xiaoyu Tang, Shuai You, Shangdong Liu, and Yimu Ji. "BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation." Applied Sciences 13, no. 18 (2023): 10244. http://dx.doi.org/10.3390/app131810244.

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Accurately segmenting the optic disk (OD) and optic cup (OC) on retinal fundus images is important for treating glaucoma. With the development of deep learning, some CNN-based methods have been implemented to segment OD and OC, but it is difficult to accurately segment OD and OC boundaries affected by blood vessels and the lesion area. To this end, we propose a novel boundary-enhanced adaptive context network (BEAC-Net) for OD and OC segmentation. Firstly, a newly designed efficient boundary pixel attention (EBPA) module enhances pixel-by-pixel feature capture to collect the boundary contextua

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Naik, B. Balaji, and R. Mariappan. "Classification of Eye Diseases Using Optic Cup Segmentation and Optic Disc Ratio." IOSR Journal of Computer Engineering 18, no. 05 (2016): 15–22. http://dx.doi.org/10.9790/0661-1805031522.

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Naik, B. Balaji, and R. Mariappan. "Classification of Eye Diseases Using Optic Cup Segmentation and Optic Disc Ratio." IOSR Journal of Computer Engineering 18, no. 05 (2016): 87–94. http://dx.doi.org/10.9790/0661-1805038794.

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Cheng, Jun, Jiang Liu, Yanwu Xu, et al. "Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening." IEEE Transactions on Medical Imaging 32, no. 6 (2013): 1019–32. http://dx.doi.org/10.1109/tmi.2013.2247770.

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Thakur, Niharika, and Mamta Juneja. "Optic disc and optic cup segmentation from retinal images using hybrid approach." Expert Systems with Applications 127 (August 2019): 308–22. http://dx.doi.org/10.1016/j.eswa.2019.03.009.

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Biswal, Birendra, Vyshnavi Eadara, Dwiti K. Bebarta, and Gupteswar Sahu. "Robust segmentation of optic disc and optic cup using statistical Kurtosis test." International Journal of Imaging Systems and Technology 30, no. 3 (2019): 527–43. http://dx.doi.org/10.1002/ima.22389.

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Bian, Xuesheng, Xiongbiao Luo, Cheng Wang, Weiquan Liu, and Xiuhong Lin. "Optic disc and optic cup segmentation based on anatomy guided cascade network." Computer Methods and Programs in Biomedicine 197 (December 2020): 105717. http://dx.doi.org/10.1016/j.cmpb.2020.105717.

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Hu, Man, Chenghao Zhu, Xiaoxing Li, and Yongli Xu. "Optic cup segmentation from fundus images for glaucoma diagnosis." Bioengineered 8, no. 1 (2016): 21–28. http://dx.doi.org/10.1080/21655979.2016.1227144.

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Qhisthana Pratika, Alva Rischa, Rita Magdalena, and R. Yunendah Nur Fuadah. "Klasifikasi Glaukoma Menggunakan Artificial Neural Network." Jurnal Ilmiah FIFO 12, no. 2 (2020): 179. http://dx.doi.org/10.22441/fifo.2020.v12i2.007.

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Abstract Glaucoma is an eye disease caused by increased eyeball pressure resulting in damage to the optic nerve and the second leading cause of blindness after cataracts. Nerve damage often occurs without symptoms so that an early examination can reduce the risk of glaucoma. Therefore, the authors designed a glaucoma detection system through eye fundal images that can facilitate the detection of glaucomaby extracting various features like Rim to Disc Ratio, Cup to Disc Ratio (CDR), Vertical Cup to Disc Ratio (VCDR), Horizontal Cup to Disc Ratio (HCDR), and Horizontal to Vertical CDR (H-V CDR)

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Wang, Soohyun, Byoungkug Kim, and Doo-Seop Eom. "Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images." Applied Sciences 15, no. 9 (2025): 5165. https://doi.org/10.3390/app15095165.

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Segmentation of the Optic Disc (OD) and Optic Cup (OC) boundaries in fundus images is a critical step for early glaucoma diagnosis, but accurate segmentation is challenging due to low boundary contrast and significant anatomical variability. To address these challenges, this study proposes a novel segmentation framework that integrates structure-preserving data augmentation, Boundary-aware Transformer Attention (BAT), and Geometry-aware Loss. We enhance data diversity while preserving vascular and tissue structures through truncated Gaussian-based sampling and colormap transformations. BAT str

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Janani, R., and S. P. Rajamohana. "Early detection of glaucoma using optic disc and optic cup segmentation: A survey." Materials Today: Proceedings 45 (2021): 2763–69. http://dx.doi.org/10.1016/j.matpr.2020.11.613.

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Tabassum, Munazza, Tariq M. Khan, Muhammad Arsalan, et al. "CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening." IEEE Access 8 (2020): 102733–47. http://dx.doi.org/10.1109/access.2020.2998635.

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Wang, Ying, Xiaosheng Yu, Jianning Chi, and Chengdong Wu. "Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two-Layer Level Set Method." Mathematical Problems in Engineering 2019 (October 24, 2019): 1–10. http://dx.doi.org/10.1155/2019/4836296.

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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, w

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V, Krishnamoorthy, Sivanantham S, Akshaya V, Nivedha S, Sivakumar Depuru, and Manikandan M. "A Novel Deep Learning Framework for Enhanced Glaucoma Detection Using Attention-Gated U-Net, Deep Wavelet Scattering, and Vision Transformers." Journal of Electronics, Electromedical Engineering, and Medical Informatics 7, no. 2 (2025): 520–32. https://doi.org/10.35882/jeeemi.v7i2.706.

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Globally, Glaucoma is a major cause of permanent blindness, and maintaining eyesight depends on early detection. Here, a brand-new deep-learning system for glaucoma prediction. In this work, we offer a novel deep-learning approach for enhanced glaucoma prediction that uses a denoising generative adversarial network for preprocessing the input image is provided, later the segmentation is carried out by Attention-Gated U-Net with Dilated Convolutions to segment the optic cup and optic disc. Feature Extraction Using a Deep Wavelet Scattering Network and finally the glaucoma classification is carr

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Yu, Linfang. "Joint segmentation of optic cup and optic disc using deep convolutional generative adversarial network." Journal of Physics: Conference Series 2234, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2234/1/012008.

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Abstract 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 genera

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