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Shalini R, Gopi VP. Multiresolution cascaded attention U-Net for localization and segmentation of optic disc and fovea in fundus images. Sci Rep 2024; 14:23107. [PMID: 39367046 PMCID: PMC11452642 DOI: 10.1038/s41598-024-73493-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 09/18/2024] [Indexed: 10/06/2024] Open
Abstract
Identification of retinal diseases in automated screening methods, such as those used in clinical settings or computer-aided diagnosis, usually depends on the localization and segmentation of the Optic Disc (OD) and fovea. However, this task is difficult since these anatomical features have irregular spatial, texture, and shape characteristics, limited sample sizes, and domain shifts due to different data distributions across datasets. This study proposes a novel Multiresolution Cascaded Attention U-Net (MCAU-Net) model that addresses these problems by optimally balancing receptive field size and computational efficiency. The MCAU-Net utilizes two skip connections to accurately localize and segment the OD and fovea in fundus images. We incorporated a Multiresolution Wavelet Pooling Module (MWPM) into the CNN at each stage of U-Net input to compensate for spatial information loss. Additionally, we integrated a cascaded connection of the spatial and channel attentions as a skip connection in MCAU-Net to concentrate precisely on the target object and improve model convergence for segmenting and localizing OD and fovea centers. The proposed model has a low parameter count of 0.8 million, improving computational efficiency and reducing the risk of overfitting. For OD segmentation, the MCAU-Net achieves high IoU values of 0.9771, 0.945, and 0.946 for the DRISHTI-GS, DRIONS-DB, and IDRiD datasets, respectively, outperforming previous results for all three datasets. For the IDRiD dataset, the MCAU-Net locates the OD center with an Euclidean Distance (ED) of 16.90 pixels and the fovea center with an ED of 33.45 pixels, demonstrating its effectiveness in overcoming the common limitations of state-of-the-art methods.
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Affiliation(s)
- R Shalini
- Department of Electronics and Communication Engineering, National Institute of Technology, 620015, Tiruchirappalli, Tamilnadu, India.
| | - Varun P Gopi
- Department of Electronics and Communication Engineering, National Institute of Technology, 620015, Tiruchirappalli, Tamilnadu, India
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Song Y, Zhang W, Zhang Y. A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5092-5117. [PMID: 38872528 DOI: 10.3934/mbe.2024225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
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 segmentation accuracy or high time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation of the OC and OD, where we have adopted fuzzy learning and a multi-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared to most state-of-the-art approaches in terms of both training time and segmentation accuracy.
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Affiliation(s)
- Yantao Song
- Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, China
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
| | - Wenjie Zhang
- Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, China
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
| | - Yue Zhang
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
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Tang S, Song C, Wang D, Gao Y, Liu Y, Lv W. W-Net: A boundary-aware cascade network for robust and accurate optic disc segmentation. iScience 2024; 27:108247. [PMID: 38230262 PMCID: PMC10790032 DOI: 10.1016/j.isci.2023.108247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/14/2023] [Accepted: 10/16/2023] [Indexed: 01/18/2024] Open
Abstract
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.
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Affiliation(s)
- Shuo Tang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Chongchong Song
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Defeng Wang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Yang Gao
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Yuchen Liu
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Wang Lv
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
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Yi Y, Jiang Y, Zhou B, Zhang N, Dai J, Huang X, Zeng Q, Zhou W. C2FTFNet: Coarse-to-fine transformer network for joint optic disc and cup segmentation. Comput Biol Med 2023; 164:107215. [PMID: 37481947 DOI: 10.1016/j.compbiomed.2023.107215] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/07/2023] [Accepted: 06/25/2023] [Indexed: 07/25/2023]
Abstract
Glaucoma is a leading cause of worldwide blindness and visual impairment, making early screening and diagnosis is crucial to prevent vision loss. Cup-to-Disk Ratio (CDR) evaluation serves as a widely applied approach for effective glaucoma screening. At present, deep learning methods have exhibited outstanding performance in optic disk (OD) and optic cup (OC) segmentation and maturely deployed in CAD system. However, owning to the complexity of clinical data, these techniques could be constrained. Therefore, an original Coarse-to-Fine Transformer Network (C2FTFNet) is designed to segment OD and OC jointly , which is composed of two stages. In the coarse stage, to eliminate the effects of irrelevant organization on the segmented OC and OD regions, we employ U-Net and Circular Hough Transform (CHT) to segment the Region of Interest (ROI) of OD. Meanwhile, a TransUnet3+ model is designed in the fine segmentation stage to extract the OC and OD regions more accurately from ROI. In this model, to alleviate the limitation of the receptive field caused by traditional convolutional methods, a Transformer module is introduced into the backbone to capture long-distance dependent features for retaining more global information. Then, a Multi-Scale Dense Skip Connection (MSDC) module is proposed to fuse the low-level and high-level features from different layers for reducing the semantic gap among different level features. Comprehensive experiments conducted on DRIONS-DB, Drishti-GS, and REFUGE datasets validate the superior effectiveness of the proposed C2FTFNet compared to existing state-of-the-art approaches.
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Affiliation(s)
- Yugen Yi
- School of Software, Jiangxi Normal University, Nanchang, 330022, China; Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance, Nanchang, 330022, China
| | - Yan Jiang
- School of Software, Jiangxi Normal University, Nanchang, 330022, China
| | - Bin Zhou
- School of Software, Jiangxi Normal University, Nanchang, 330022, China
| | - Ningyi Zhang
- School of Software, Jiangxi Normal University, Nanchang, 330022, China
| | - Jiangyan Dai
- School of Computer Engineering, Weifang University, 261061, China.
| | - Xin Huang
- School of Software, Jiangxi Normal University, Nanchang, 330022, China
| | - Qinqin Zeng
- Department of Ophthalmology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Wei Zhou
- College of Computer Science, Shenyang Aerospace University, Shenyang, 110136, China.
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Zedan MJM, Zulkifley MA, Ibrahim AA, Moubark AM, Kamari NAM, Abdani SR. Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review. Diagnostics (Basel) 2023; 13:2180. [PMID: 37443574 DOI: 10.3390/diagnostics13132180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 07/15/2023] Open
Abstract
Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner.
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Affiliation(s)
- Mohammad J M Zedan
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
- Computer and Information Engineering Department, College of Electronics Engineering, Ninevah University, Mosul 41002, Iraq
| | - Mohd Asyraf Zulkifley
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Ahmad Asrul Ibrahim
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Asraf Mohamed Moubark
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Nor Azwan Mohamed Kamari
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Siti Raihanah Abdani
- School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia
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