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Lepetit-Aimon G, Playout C, Boucher MC, Duval R, Brent MH, Cheriet F. MAPLES-DR: MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy. Sci Data 2024; 11:914. [PMID: 39179588 PMCID: PMC11343847 DOI: 10.1038/s41597-024-03739-6] [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: 12/20/2023] [Accepted: 08/05/2024] [Indexed: 08/26/2024] Open
Abstract
Reliable automatic diagnosis of Diabetic Retinopathy (DR) and Macular Edema (ME) is an invaluable asset in improving the rate of monitored patients among at-risk populations and in enabling earlier treatments before the pathology progresses and threatens vision. However, the explainability of screening models is still an open question, and specifically designed datasets are required to support the research. We present MAPLES-DR (MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy), which contains, for 198 images of the MESSIDOR public fundus dataset, new diagnoses for DR and ME as well as new pixel-wise segmentation maps for 10 anatomical and pathological biomarkers related to DR. This paper documents the design choices and the annotation procedure that produced MAPLES-DR, discusses the interobserver variability and the overall quality of the annotations, and provides guidelines on using the dataset in a machine learning context.
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Affiliation(s)
- Gabriel Lepetit-Aimon
- Department of Computer and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada.
| | - Clément Playout
- Department of Ophthalmology, Université de Montréal, Montréal, Canada
- Centre Universitaire d'Ophtalmologie, Hôpital Maisonneuve-Rosemont, Montréal, Canada
| | - Marie Carole Boucher
- Department of Ophthalmology, Université de Montréal, Montréal, Canada
- Centre Universitaire d'Ophtalmologie, Hôpital Maisonneuve-Rosemont, Montréal, Canada
| | - Renaud Duval
- Department of Ophthalmology, Université de Montréal, Montréal, Canada
- Centre Universitaire d'Ophtalmologie, Hôpital Maisonneuve-Rosemont, Montréal, Canada
| | - Michael H Brent
- Department of Ophthalmology and Vision Science, University of Toronto, Toronto, Canada
| | - Farida Cheriet
- Department of Computer and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada
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2
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Wei H, Shi P, Miao J, Zhang M, Bai G, Qiu J, Liu F, Yuan W. CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading. Comput Biol Med 2024; 175:108459. [PMID: 38701588 DOI: 10.1016/j.compbiomed.2024.108459] [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: 08/31/2023] [Revised: 03/31/2024] [Accepted: 04/07/2024] [Indexed: 05/05/2024]
Abstract
Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR. Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR.
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Affiliation(s)
- Hao Wei
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
| | - Peilun Shi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Juzheng Miao
- Department of Computer Science Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Mingqin Zhang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Guitao Bai
- Department of Ophthalmology, Zigong First People's Hospital, ZiGong, China
| | - Jianing Qiu
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Furui Liu
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Wu Yuan
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
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Zhao X, Chen S, Zhang S, Liu Y, Hu Y, Yuan D, Xie L, Luo X, Zheng M, Tian R, Chen Y, Tan T, Yu Z, Sun Y, Wu Z, Zhang G. A fundus image dataset for intelligent retinopathy of prematurity system. Sci Data 2024; 11:543. [PMID: 38802420 PMCID: PMC11130119 DOI: 10.1038/s41597-024-03362-5] [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: 12/13/2023] [Accepted: 05/10/2024] [Indexed: 05/29/2024] Open
Abstract
Image-based artificial intelligence (AI) systems stand as the major modality for evaluating ophthalmic conditions. However, most of the currently available AI systems are designed for experimental research using single-central datasets. Most of them fell short of application in real-world clinical settings. In this study, we collected a dataset of 1,099 fundus images in both normal and pathologic eyes from 483 premature infants for intelligent retinopathy of prematurity (ROP) system development and validation. Dataset diversity was visualized with a spatial scatter plot. Image classification was conducted by three annotators. To the best of our knowledge, this is one of the largest fundus datasets on ROP, and we believe it is conducive to the real-world application of AI systems.
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Affiliation(s)
- Xinyu Zhao
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Shaobin Chen
- Faculty of Applied Sciences, Macao Polytechnic University, Macao Special Administrative Region of China, Macao, China
| | - Sifan Zhang
- Department of Biology, New York University, New York, NY, US
| | - Yaling Liu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Yarou Hu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Duo Yuan
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Liqiong Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
| | - Xiayuan Luo
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Mianying Zheng
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Ruyin Tian
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Yi Chen
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao Special Administrative Region of China, Macao, China
| | - Zhen Yu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Yue Sun
- Faculty of Applied Sciences, Macao Polytechnic University, Macao Special Administrative Region of China, Macao, China.
| | - Zhenquan Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
| | - Guoming Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
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Huang Y, Lyu J, Cheng P, Tam R, Tang X. SSiT: Saliency-Guided Self-Supervised Image Transformer for Diabetic Retinopathy Grading. IEEE J Biomed Health Inform 2024; 28:2806-2817. [PMID: 38319784 DOI: 10.1109/jbhi.2024.3362878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised image Transformer (SSiT) is proposed for Diabetic Retinopathy (DR) grading from fundus images. We novelly introduce saliency maps into SSL, with a goal of guiding self-supervised pre-training with domain-specific prior knowledge. Specifically, two saliency-guided learning tasks are employed in SSiT: 1) Saliency-guided contrastive learning is conducted based on the momentum contrast, wherein fundus images' saliency maps are utilized to remove trivial patches from the input sequences of the momentum-updated key encoder. Thus, the key encoder is constrained to provide target representations focusing on salient regions, guiding the query encoder to capture salient features. 2) The query encoder is trained to predict the saliency segmentation, encouraging the preservation of fine-grained information in the learned representations. To assess our proposed method, four publicly-accessible fundus image datasets are adopted. One dataset is employed for pre-training, while the three others are used to evaluate the pre-trained models' performance on downstream DR grading. The proposed SSiT significantly outperforms other representative state-of-the-art SSL methods on all downstream datasets and under various evaluation settings. For example, SSiT achieves a Kappa score of 81.88% on the DDR dataset under fine-tuning evaluation, outperforming all other ViT-based SSL methods by at least 9.48%.
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Gonçalves MB, Nakayama LF, Ferraz D, Faber H, Korot E, Malerbi FK, Regatieri CV, Maia M, Celi LA, Keane PA, Belfort R. Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review. Eye (Lond) 2024; 38:426-433. [PMID: 37667028 PMCID: PMC10858054 DOI: 10.1038/s41433-023-02717-3] [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: 03/08/2023] [Revised: 06/26/2023] [Accepted: 08/25/2023] [Indexed: 09/06/2023] Open
Abstract
This study aimed to evaluate the image quality assessment (IQA) and quality criteria employed in publicly available datasets for diabetic retinopathy (DR). A literature search strategy was used to identify relevant datasets, and 20 datasets were included in the analysis. Out of these, 12 datasets mentioned performing IQA, but only eight specified the quality criteria used. The reported quality criteria varied widely across datasets, and accessing the information was often challenging. The findings highlight the importance of IQA for AI model development while emphasizing the need for clear and accessible reporting of IQA information. The study suggests that automated quality assessments can be a valid alternative to manual labeling and emphasizes the importance of establishing quality standards based on population characteristics, clinical use, and research purposes. In conclusion, image quality assessment is important for AI model development; however, strict data quality standards must not limit data sharing. Given the importance of IQA for developing, validating, and implementing deep learning (DL) algorithms, it's recommended that this information be reported in a clear, specific, and accessible way whenever possible. Automated quality assessments are a valid alternative to the traditional manual labeling process, and quality standards should be determined according to population characteristics, clinical use, and research purpose.
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Affiliation(s)
- Mariana Batista Gonçalves
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Luis Filipe Nakayama
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil.
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA, USA.
| | - Daniel Ferraz
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Hanna Faber
- Department of Ophthalmology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Ophthalmology, University of Tuebingen, Tuebingen, Germany
| | - Edward Korot
- Retina Specialists of Michigan, Grand Rapids, MI, USA
- Stanford University Byers Eye Institute Palo Alto, Palo Alto, CA, USA
| | | | | | - Mauricio Maia
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
| | - Leo Anthony Celi
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA, USA
- Harvard TH Chan School of Public Health, Department of Biostatistics, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Department of Medicine, Boston, MA, USA
| | - Pearse A Keane
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Rubens Belfort
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
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Fang H, Li F, Wu J, Fu H, Sun X, Orlando JI, Bogunović H, Zhang X, Xu Y. Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation. Sci Data 2024; 11:99. [PMID: 38245589 PMCID: PMC10799845 DOI: 10.1038/s41597-024-02911-2] [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: 10/12/2022] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.
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Affiliation(s)
- Huihui Fang
- South China University of Technology, Guangzhou, China
- Pazhou Lab., Guangzhou, China
| | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Junde Wu
- National University of Singapore, Singapore, Singapore
| | - Huazhu Fu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore
| | - Xu Sun
- Pazhou Lab., Guangzhou, China
| | | | - Hrvoje Bogunović
- Christian Doppler Lab for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
| | - Yanwu Xu
- South China University of Technology, Guangzhou, China.
- Pazhou Lab., Guangzhou, China.
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7
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Lin L, Peng L, He H, Cheng P, Wu J, Wong KKY, Tang X. YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation. Med Image Anal 2023; 90:102937. [PMID: 37672901 DOI: 10.1016/j.media.2023.102937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 06/30/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023]
Abstract
Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging task due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg. A very essential component of YoloCurvSeg is image synthesis. Specifically, a background generator delivers image backgrounds that closely match the real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.
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Affiliation(s)
- Li Lin
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
| | - Linkai Peng
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Huaqing He
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
| | - Pujin Cheng
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
| | - Jiewei Wu
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Kenneth K Y Wong
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
| | - Xiaoying Tang
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China.
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8
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Yang W, Zhang X, Ma B, Wang Y, Wu Y, Yan J, Liu Y, Zhang C, Wan J, Wang Y, Huang M, Li Y, Zhao D. An open dataset for intelligent recognition and classification of abnormal condition in longwall mining. Sci Data 2023; 10:416. [PMID: 37369715 PMCID: PMC10300123 DOI: 10.1038/s41597-023-02322-9] [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: 11/28/2022] [Accepted: 06/20/2023] [Indexed: 06/29/2023] Open
Abstract
The underground coal mine production of the fully mechanized mining face exists many problems, such as poor operating environment, high accident rate and so on. Recently, the intelligent autonomous coal mining is gradually replacing the traditional mining process. The artificial intelligence technology is an active research area and is expect to identify and warn the underground abnormal conditions for intelligent longwall mining. It is inseparable from the construction of datasets, but the downhole dataset is still blank at present. This work develops an image dataset of underground longwall mining face (DsLMF+), which consists of 138004 images with annotation 6 categories of mine personnel, hydraulic support guard plate, large coal, towline, miners' behaviour and mine safety helmet. All the labels of dataset are publicly available in YOLO format and COCO format. The availability and accuracy of the datasets were reviewed by experts in coal mine field. The dataset is open access and aims to support further research and advancement of the intelligent identification and classification of abnormal conditions for underground mining.
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Affiliation(s)
- Wenjuan Yang
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
- Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, No.58, Yanta Road, Xi'an, 710054, China
| | - Xuhui Zhang
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China.
- Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, No.58, Yanta Road, Xi'an, 710054, China.
| | - Bing Ma
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
| | - Yanqun Wang
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
| | - Yujia Wu
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
| | - Jianxing Yan
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
| | - Yongwei Liu
- MARCO automatic control system development Co.,LTD, No.20, Fenghui South Road, Xi'an, 710054, China
| | - Chao Zhang
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
| | - Jicheng Wan
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
| | - Yue Wang
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
| | - Mengyao Huang
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
| | - Yuyang Li
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
| | - Dian Zhao
- School of Mechanical Engineering, Xi'an University of Science and Technology, No.58, Mid-Yanta Road, Xi'an, 710054, China
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9
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Krzywicki T, Brona P, Zbrzezny AM, Grzybowski AE. A Global Review of Publicly Available Datasets Containing Fundus Images: Characteristics, Barriers to Access, Usability, and Generalizability. J Clin Med 2023; 12:jcm12103587. [PMID: 37240693 DOI: 10.3390/jcm12103587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/29/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
This article provides a comprehensive and up-to-date overview of the repositories that contain color fundus images. We analyzed them regarding availability and legality, presented the datasets' characteristics, and identified labeled and unlabeled image sets. This study aimed to complete all publicly available color fundus image datasets to create a central catalog of available color fundus image datasets.
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Affiliation(s)
- Tomasz Krzywicki
- Faculty of Mathematics and Computer Science, University of Warmia and Mazury, 10-710 Olsztyn, Poland
| | - Piotr Brona
- Department of Ophthalmology, Poznan City Hospital, 61-285 Poznań, Poland
| | - Agnieszka M Zbrzezny
- Faculty of Mathematics and Computer Science, University of Warmia and Mazury, 10-710 Olsztyn, Poland
- Faculty of Design, SWPS University of Social Sciences and Humanities, Chodakowska 19/31, 03-815 Warsaw, Poland
| | - Andrzej E Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, 60-836 Poznań, Poland
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Huang Y, Lin L, Cheng P, Lyu J, Tam R, Tang X. Identifying the Key Components in ResNet-50 for Diabetic Retinopathy Grading from Fundus Images: A Systematic Investigation. Diagnostics (Basel) 2023; 13:diagnostics13101664. [PMID: 37238149 DOI: 10.3390/diagnostics13101664] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 04/29/2023] [Accepted: 05/01/2023] [Indexed: 05/28/2023] Open
Abstract
Although deep learning-based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on prediction performance. The training setting includes various interdependent components, such as an objective function, a data sampling strategy, and a data augmentation approach. To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components. Extensive experiments are conducted on a publicly available dataset EyePACS. We demonstrate that (1) the DR grading framework is sensitive to input resolution, objective function, and composition of data augmentation; (2) using mean square error as the loss function can effectively improve the performance with respect to a task-specific evaluation metric, namely the quadratically weighted Kappa; (3) utilizing eye pairs boosts the performance of DR grading and; (4) using data resampling to address the problem of imbalanced data distribution in EyePACS hurts the performance. Based on these observations and an optimal combination of the investigated components, our framework, without any specialized network design, achieves a state-of-the-art result (0.8631 for Kappa) on the EyePACS test set (a total of 42,670 fundus images) with only image-level labels. We also examine the proposed training practices on other fundus datasets and other network architectures to evaluate their generalizability. Our codes and pre-trained model are available online.
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Affiliation(s)
- Yijin Huang
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Li Lin
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Pujin Cheng
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Junyan Lyu
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Roger Tam
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Xiaoying Tang
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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Exudate identification in retinal fundus images using precise textural verifications. Sci Rep 2023; 13:2824. [PMID: 36808177 PMCID: PMC9938199 DOI: 10.1038/s41598-023-29916-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 02/13/2023] [Indexed: 02/19/2023] Open
Abstract
One of the most salient diseases of retina is Diabetic Retinopathy (DR) which may lead to irreparable damages to eye vision in the advanced phases. A large number of the people infected with diabetes experience DR. The early identification of DR signs facilitates the treatment process and prevents from blindness. Hard Exudates (HE) are bright lesions appeared in retinal fundus images of DR patients. Thus, the detection of HEs is an important task preventing the progress of DR. However, the detection of HEs is a challenging process due to their different appearance features. In this paper, an automatic method for the identification of HEs with various sizes and shapes is proposed. The method works based on a pixel-wise approach. It considers several semi-circular regions around each pixel. For each semi-circular region, the intensity changes around several directions and non-necessarily equal radiuses are computed. All pixels for which several semi-circular regions include considerable intensity changes are considered as the pixels located in HEs. In order to reduce false positives, an optic disc localization method is proposed in the post-processing phase. The performance of the proposed method has been evaluated on DIARETDB0 and DIARETDB1 datasets. The experimental results confirm the improved performance of the suggested method in term of accuracy.
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Bhati A, Gour N, Khanna P, Ojha A. Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset. Comput Biol Med 2023; 153:106519. [PMID: 36608462 DOI: 10.1016/j.compbiomed.2022.106519] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 12/23/2022] [Accepted: 12/31/2022] [Indexed: 01/04/2023]
Abstract
It is feasible to recognize the presence and seriousness of eye disease by investigating the progressions in retinal biological structures. Fundus examination is a diagnostic procedure to examine the biological structure and anomalies present in the eye. Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. Ocular Disease Intelligent Recognition (ODIR-5K) is a benchmark structured fundus image dataset utilized by researchers for multi-label multi-disease classification of fundus images. This work presents a Discriminative Kernel Convolution Network (DKCNet), which explores discriminative region-wise features without adding extra computational cost. DKCNet is composed of an attention block followed by a Squeeze-and-Excitation (SE) block. The attention block takes features from the backbone network and generates discriminative feature attention maps. The SE block takes the discriminative feature maps and improves channel interdependencies. Better performance of DKCNet is observed with InceptionResnet backbone network for multi-label classification of ODIR-5K fundus images with 96.08 AUC, 94.28 F1-score, and 0.81 kappa score. The proposed method splits the common target label for an eye pair based on the diagnostic keyword. Based on these labels, over-sampling and/or under-sampling are done to resolve the class imbalance. To check the bias of the proposed model towards training data, the model trained on the ODIR dataset is tested on three publicly available benchmark datasets. It is observed that the proposed DKCNet gives good performance on completely unseen fundus images also.
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Affiliation(s)
- Amit Bhati
- Departement of Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, India
| | - Neha Gour
- Departement of Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, India
| | - Pritee Khanna
- Departement of Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, India.
| | - Aparajita Ojha
- Departement of Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, India
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Hertzano R, Mahurkar A. Advancing discovery in hearing research via biologist-friendly access to multi-omic data. Hum Genet 2022; 141:319-322. [PMID: 35235019 PMCID: PMC9034999 DOI: 10.1007/s00439-022-02445-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 02/24/2022] [Indexed: 01/01/2023]
Abstract
High-throughput cell type-specific multi-omic analyses have advanced our understanding of inner ear biology in an unprecedented way. The full benefit of these data, however, is reached from their re-use. Successful re-use of data requires identifying the natural users and ensuring proper data democratization and federation for their seamless and meaningful access. Here we discuss universal challenges in access and re-use of multi-omic data, possible solutions, and introduce the gEAR (the gene Expression Analysis Resource, umgear.org)-a tool for multi-omic data visualization, sharing and access for the ear field.
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Affiliation(s)
- Ronna Hertzano
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
| | - Anup Mahurkar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
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Lin L, Li M, Huang Y, Cheng P, Xia H, Wang K, Yuan J, Tang X. The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading. Sci Data 2020; 7:409. [PMID: 33219237 PMCID: PMC7679367 DOI: 10.1038/s41597-020-00755-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/03/2020] [Indexed: 11/09/2022] Open
Abstract
Automated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of annotations, which is the fundamental motivation of this work. In this study, we construct a dataset containing 1219 fundus images (from DR patients and healthy controls) with annotations of exudate lesions. In addition to exudate annotations, we also provide four additional labels for each image: left-versus-right eye label, DR grade (severity scale) from three different grading protocols, the bounding box of the optic disc (OD), and fovea location. This dataset provides a great opportunity to analyze the accuracy and reliability of different exudate detection, OD detection, fovea localization, and DR classification algorithms. Moreover, it will facilitate the development of such algorithms in the realm of supervised and semi-supervised learning.
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Affiliation(s)
- Li Lin
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 5180000, China.,School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510000, China
| | - Meng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, 510000, China
| | - Yijin Huang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 5180000, China
| | - Pujin Cheng
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 5180000, China
| | - Honghui Xia
- Department of Ophthalmology, Gaoyao People's Hospital, Zhaoqing, 526000, China
| | - Kai Wang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510000, China
| | - Jin Yuan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, 510000, China.
| | - Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 5180000, China.
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