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Zhou Z, Zheng Y, Zhou X, Yu J, Rong S. Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network. BMC Ophthalmol 2024; 24:98. [PMID: 38438876 PMCID: PMC10910696 DOI: 10.1186/s12886-024-03376-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/28/2024] [Indexed: 03/06/2024] Open
Abstract
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 D C disc and 0.8976 D C cup . The code will be made publicly available.
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Affiliation(s)
- Zhiwang Zhou
- Institute for Advanced Study, Nanchang University, Nanchang, 330031, China.
| | - Yuanchang Zheng
- Institute for Advanced Study, Nanchang University, Nanchang, 330031, China
- Institute of Science and Technology, Waseda University, Tokyo, 63-8001, Japan
| | - Xiaoyu Zhou
- School of Transportation Engineering, Tongji University, Shanghai, 200000, China
| | - Jie Yu
- School of Electrical Automation and Information Engineering, Tianjin University, Tianjin, 300000, China
| | - Shangjie Rong
- School of Mathematical Sciences, Xiamen University, Xiamen, 361000, China
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2
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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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3
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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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Alfonso-Francia G, Pedraza-Ortega JC, Badillo-Fernández M, Toledano-Ayala M, Aceves-Fernandez MA, Rodriguez-Resendiz J, Ko SB, Tovar-Arriaga S. Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs. Diagnostics (Basel) 2022; 12:diagnostics12123031. [PMID: 36553037 PMCID: PMC9777130 DOI: 10.3390/diagnostics12123031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/22/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder-Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.
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Affiliation(s)
- Gendry Alfonso-Francia
- Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro 76010, Mexico
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada
| | | | - Mariana Badillo-Fernández
- Instituto Mexicano de Oftalmología (IMO) I.A.P., Circuito Exterior Estadio Corregidora sn, Centro Sur, Santiago de Querétaro 76010, Mexico
| | - Manuel Toledano-Ayala
- Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro 76010, Mexico
| | | | | | - Seok-Bum Ko
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada
| | - Saul Tovar-Arriaga
- Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro 76010, Mexico
- Correspondence:
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Peng Y, Zhu W, Chen Z, Shi F, Wang M, Zhou Y, Wang L, Shen Y, Xiang D, Chen F, Chen X. AFENet: Attention Fusion Enhancement Network for Optic Disc Segmentation of Premature Infants. Front Neurosci 2022; 16:836327. [PMID: 35516802 PMCID: PMC9063315 DOI: 10.3389/fnins.2022.836327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/09/2022] [Indexed: 11/16/2022] Open
Abstract
Retinopathy of prematurity and ischemic brain injury resulting in periventricular white matter damage are the main causes of visual impairment in premature infants. Accurate optic disc (OD) segmentation has important prognostic significance for the auxiliary diagnosis of the above two diseases of premature infants. Because of the complexity and non-uniform illumination and low contrast between background and the target area of the fundus images, the segmentation of OD for infants is challenging and rarely reported in the literature. In this article, to tackle these problems, we propose a novel attention fusion enhancement network (AFENet) for the accurate segmentation of OD in the fundus images of premature infants by fusing adjacent high-level semantic information and multiscale low-level detailed information from different levels based on encoder-decoder network. Specifically, we first design a dual-scale semantic enhancement (DsSE) module between the encoder and the decoder inspired by self-attention mechanism, which can enhance the semantic contextual information for the decoder by reconstructing skip connection. Then, to reduce the semantic gaps between the high-level and low-level features, a multiscale feature fusion (MsFF) module is developed to fuse multiple features of different levels at the top of encoder by using attention mechanism. Finally, the proposed AFENet was evaluated on the fundus images of preterm infants for OD segmentation, which shows that the proposed two modules are both promising. Based on the baseline (Res34UNet), using DsSE or MsFF module alone can increase Dice similarity coefficients by 1.51 and 1.70%, respectively, whereas the integration of the two modules together can increase 2.11%. Compared with other state-of-the-art segmentation methods, the proposed AFENet achieves a high segmentation performance.
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Affiliation(s)
- Yuanyuan Peng
- Analysis and Visualization Lab, School of Electronics and Information Engineering and Medical Image Processing, Soochow University, Suzhou, China
| | - Weifang Zhu
- Analysis and Visualization Lab, School of Electronics and Information Engineering and Medical Image Processing, Soochow University, Suzhou, China
| | - Zhongyue Chen
- Analysis and Visualization Lab, School of Electronics and Information Engineering and Medical Image Processing, Soochow University, Suzhou, China
| | - Fei Shi
- Analysis and Visualization Lab, School of Electronics and Information Engineering and Medical Image Processing, Soochow University, Suzhou, China
| | - Meng Wang
- Analysis and Visualization Lab, School of Electronics and Information Engineering and Medical Image Processing, Soochow University, Suzhou, China
| | - Yi Zhou
- Analysis and Visualization Lab, School of Electronics and Information Engineering and Medical Image Processing, Soochow University, Suzhou, China
| | - Lianyu Wang
- Analysis and Visualization Lab, School of Electronics and Information Engineering and Medical Image Processing, Soochow University, Suzhou, China
| | - Yuhe Shen
- Analysis and Visualization Lab, School of Electronics and Information Engineering and Medical Image Processing, Soochow University, Suzhou, China
| | - Daoman Xiang
- Guangzhou Women and Children’s Medical Center, Guangzhou, China
| | - Feng Chen
- Guangzhou Women and Children’s Medical Center, Guangzhou, China
| | - Xinjian Chen
- Analysis and Visualization Lab, School of Electronics and Information Engineering and Medical Image Processing, Soochow University, Suzhou, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
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Alawad M, Aljouie A, Alamri S, Alghamdi M, Alabdulkader B, Alkanhal N, Almazroa A. Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation - A Review. Clin Ophthalmol 2022; 16:747-764. [PMID: 35300031 PMCID: PMC8923700 DOI: 10.2147/opth.s348479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 02/11/2022] [Indexed: 12/12/2022] Open
Abstract
Background Globally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs. Methods A systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed. Results Eight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation. Conclusion We provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.
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Affiliation(s)
- Mohammed Alawad
- Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrhman Aljouie
- Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Suhailah Alamri
- Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia
- Research Labs, National Center for Artificial Intelligence, Riyadh, Saudi Arabia
| | - Mansour Alghamdi
- Department of Optometry and Vision Sciences College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Balsam Alabdulkader
- Department of Optometry and Vision Sciences College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Norah Alkanhal
- Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia
| | - Ahmed Almazroa
- Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia
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Yu X, Wang Y, Wang S, Hu N. Fully Convolutional Network and Visual Saliency-Based Automatic Optic Disc Detection in Retinal Fundus Images. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:3561134. [PMID: 34512935 PMCID: PMC8424246 DOI: 10.1155/2021/3561134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/26/2021] [Accepted: 08/19/2021] [Indexed: 11/18/2022]
Abstract
We present in this paper a novel optic disc detection method based on a fully convolutional network and visual saliency in retinal fundus images. Firstly, we employ the morphological reconstruction-based object detection method to locate the optic disc region roughly. According to the location result, a 400 × 400 image patch that covers the whole optic disc is obtained by cropping the original retinal fundus image. Secondly, the Simple Linear Iterative Cluster approach is utilized to segment such an image patch into many smaller superpixels. Thirdly, each superpixel is assigned a uniform initial saliency value according to the background prior information based on the assumption that the superpixels located on the boundary of the image belong to the background. Meanwhile, we use a pretrained fully convolutional network to extract the deep features from different layers of the network and design the strategy to represent each superpixel by the deep features. Finally, both the background prior information and the deep features are integrated into the single-layer cellular automata framework to gain the accurate optic disc detection result. We utilize the DRISHTI-GS dataset and RIM-ONE r3 dataset to evaluate the performance of our method. The experimental results demonstrate that the proposed method can overcome the influence of intensity inhomogeneity, weak contrast, and the complex surroundings of the optic disc effectively and has superior performance in terms of accuracy and robustness.
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Affiliation(s)
- Xiaosheng Yu
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Ying Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Siqi Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Nan Hu
- School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China
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