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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges

arXiv

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arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

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Mesenchymal stem cells-derived exosomes ameliorate blue light stimulation in retinal pigment epithelium cells and retinal laser injury by VEGF-dependent mechanism

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International Journal of ophthalmology(English edition) 2018年第4期11卷 559-566页

作者： Guang-Hui He Wei Zhang Ying-Xue Ma Jing Yang Li Chen Jian Song Song Chen Tianjin Eye Hospital Tianjin Key Lab of Ophthalmologyand Visual ScienceTianjin Eye InstituteClinical College of OphthalmologyTianjin Medical UniversityTianjin 300020China

AIM： To observe the effect of exosomes derived from human umbilical cord blood mesenchymal stem cells（h UCMSCs） on the expression of vascular endothelial growth factor-A（VEGF-A） in blue light injured human retinal pigment epithelial（RPE） cells and laser-induced choroidal neovascularization（CNV） in ***： Exosomes were isolated from h UCMSCs and characterized by transmission electron microscope and Western blot. MSCs-derived exosomes were cultured with RPE cells exposed to blue light. The m RNA and protein expression of VEGF-A were determined by real time-polymerase chain reaction（PCR） and Western blot, respectively. Immunofluorescence assay was used for the detection of the expression level of VEGF-A. We injected different doses of MSCs-derived exosomes intravitreally to observe and compare their effects in a mouse model of laserinduced retinal injury. The histological structure of CNV in rats was inspected by hematoxylin-eosin（HE） staining and fundus fluorescein angiography. The expression of VEGF-A was detected by ***： Exosomes exhibited the typical characteristic morphology（cup-shaped） and size（diameter between 50 and 150 nm）. The exosomes marker, CD63, and h UCMSCs marker, CD90, showed a robust presence. In vitro, MSCsderived exosomes downregulated the m RNA（Exo-L： t=6.485, 7.959, 9.286; Exo-M： t=7.517, 10.170, 13.413; Exo-H： t=10.317, 12.234, 14.592, P〈0.05） and protein（Exo-L： t=2.945, 4.477, 6.657; Exo-M： t=4.713, 6.421, 8.836; Exo-H：t=6.539, 12.194, 12.783; P〈0.05） expression of VEGF-A in RPE cells after blue light stimulation. In vivo, we found that the MSCs-derived exosomes reduced damage, distinctly downregulated VEGF-A（Exo-H： t=0.957, 1.382; P〈0.05）, and gradually improved the histological structures of CNV for a better visual function（Exo-L： 0.346, Exo-M： 3.382, Exo-H： 8.571; P〈0.05）. CONCLUSION： MSCs-derived exosomes ameliorate blue light stimulation in RPE cells and laser-induced retinal injury via downregulation of VEGF

关键词： exosome human umbilical cord mesenchymal stem cell retinal pigment epithelial cell choroidal neovascularization vascular endothelial growth factor

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Segmentaion of Parapapillary Atrophy in Retinal Images using HED

Segmentaion of Parapapillary Atrophy in Retinal Images using...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Yunlong Feng Hanxiang Li Jie Xu Huiqi Li School of Information and Electronics Beijing Institute of Technology Beijing Ophthalmology and Visual Science Key Lab Beijing Institute of Ophthalmology Beijing Tongren Hospital Beijing China

Parapapillary atrophy (PPA) is a kind of atrophic abnormality of eyes, which lies on the retinal fundus adjacent to optic disc (OD). Clinically, PPA is correlated with several pathologies of eyes. For example, the area of PPA has a strong association with the severity of myopia. In this work, we developed a new segmentation algorithm aimed at PPA in retinal images. The frame work was derived from Holistically-Nested Edge Detection (HED), which originally deals with edge detection. Firstly, extracting region of interest (ROI) and normalizing datasets were performed in preprocessing stage. Next, an end-to-end model began to acquire rich hierarchical features with deep supervision on side outputs. We applied a new balance parameter to loss function for realization of the area segmentation. Finally, the holistic image predictions (PPA area segmented) were performed through full convolutional neural layers. The model training on retinal images with annotation from professional ophthalmologist achieves great performance on both adults and pupils datasets. Experiential results present the average F-score of 0.7910 on adults' test data (200 images) and 0.7478 on pupils' (50 images). MIoU of the segmentation reaches 0.7237 of adults and 0.6892 of pupils.

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Refinement of Parapapillary Atrophy Segmentation Based on Conditional Random Field

Refinement of Parapapillary Atrophy Segmentation Based on Co...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Xi Zhang Hanxiang Li Jie Xu Huiqi Li School of Information and Electronics Beijing Institute of Technology Beijing Ophthalmology and Visual Science Key Lab Beijing Institute of Ophthalmology Beijing Tongren Hospital Beijing China

Parapapillary atrophy (PPA) is a crescent atrophic abnormality adjacent to optic disk (OD). In clinical findings, PPA is highly related to glaucoma and high myopia, which makes it a potential indicator for ophthalmic diseases. Therefore, it is significant to develop an algorithm for automatic segmentation of PPA so as to aid the treatment and the prevention of eye diseases. A method that combines the confidence map with conditional random field (CRF) model is proposed in this paper to achieve the optimization of initial segmentation results from neural network. Holistically-Nested Edge Detection (HED) is applied to the segmentation of PPA and a confidence map is obtained. The CRF model is constructed with the confidence map and Maximum a posteriori (MAP) inference is implemented to conduct the segmentation. Post-processing is utilized to further refine the segmentation result. The proposed method has the advantage of robust performance when there is difference between training set and test set. The experimental results show that the average F-score is increased from 0.57 to 0.67, which shows a fair improvement effect.

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促进临床诊断的自动超广角眼底图像增强系统开发和验证——一项跨区域多中心研究（英文）

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Engineering 2024年第10期 179-188页

作者：韦巧玲谷卓遥谭伟敏孔虹雨付浩蒋沁庄文娟张少弛封利霞刘勇李甦雁秦兵陆培荣赵江月李志刚袁松涛严宏章淑杰竺向佳洪佳旭赵晨颜波 Department of Ophthalmology Eye & ENT Hospital of Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University The Affiliated Eye Hospital of Nanjing Medical University Ningxia Eye Hospital People's Hospital of Ningxia Hui Autonomous Region Third Clinical Medical College of Ningxia Medical University Department of Ophthalmology The First Affiliated Hospital of Anhui Medical University Southwest Hospital/Southwest Eye Hospital Third Military Medical University (Army Medical University) Key Lab of Visual Damage and Regeneration Restoration of Chongqing Department of Ophthalmology The Affiliated Xuzhou Municipal Hospital of Xuzhou Medical University Department of Ophthalmology Suqian First Hospital of Jiangsu Province Hospital Department of Ophthalmology The First Affiliated Hospital of Soochow University Department of Ophthalmology The Fourth Affiliated Hospital of China Medical University Eye Hospital of China Medical University Department of Ophthalmology The First Affiliated Hospital of Zhengzhou University Department of Ophthalmology Jiangsu Province Hospital Shaanxi Eye Hospital Xi'an People's Hospital (Xi'an Fourth Hospital) The Affiliated People's Hospital of Northwest University

In ophthalmology, the quality of fundus images is critical for accurate diagnosis, both in clinical practice and in artificial intelligence(AI)-assisted diagnostics. Despite the broad view provided by ultrawide-field(UWF) imaging, pseudocolor images may conceal critical lesions necessary for precise diagnosis. To address this, we introduce UWF-Net, a sophisticated image enhancement algorithm that takes disease characteristics into consideration. Using the Fudan University ultra-wide-field image(FDUWI) dataset,which includes 11 294 Optos pseudocolor and 2415 Zeiss true-color UWF images, each of which is rigorously annotated, UWF-Net combines global style modeling with feature-level lesion *** consistency loss is also applied to maintain fundus feature integrity, significantly improving image quality. Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE) and structure and illumination constrained generative adversarial network(Still GAN), delivering superior retinal image quality,higher quality scores, and preserved feature details after enhancement. In disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by Still GAN, demonstrating a 4.62% increase in sensitivity(SEN) and a 3.97% increase in accuracy(ACC). In a multicenter clinical setting, UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors, and yielded a significant reduction in diagnostic time((13.17 ± 8.40) s for UWF-Net enhanced images vs(19.54 ± 12.40) s for original images) and an increase in diagnostic accuracy(87.71% for UWF-Net enhanced images vs 80.40% for original images). Our research verifies that UWF-Net markedly improves the quality of UWF imaging, facilitating better clinical outcomes and more reliable AI-assisted disease classification. The clin

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Corrigendum to 'Recent progress in optical clearing of eye tissues' [Exp. Eye Res. (Nov;2021) 212 108796]

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Experimental eye research 2022年 214卷 108896页

作者： Wenhan Lu Zhenle Pei Wei Hu Chen Tan Xiaoyu Tong Yi Feng Xinghuai Sun Department of Ophthalmology & Visual Science Eye & ENT Hospital Shanghai Medical College Fudan University Shanghai 200031 China Department of Integrative Medicine and Neurobiology State Key Lab of Medical Neurobiology Institute of Integrative Medicine of Fudan University Institute of Brain Science School of Basic Medical Sciences Fudan University Shanghai 200032 China. Department of Ophthalmology & Visual Science Eye & ENT Hospital Shanghai Medical College Fudan University Shanghai 200031 China. Department of Integrative Medicine and Neurobiology State Key Lab of Medical Neurobiology Institute of Integrative Medicine of Fudan University Institute of Brain Science School of Basic Medical Sciences Fudan University Shanghai 200032 China. Electronic address: fengyi17@***. State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China NHC Key Laboratory of Myopia Chinese Academy of Medical Sciences And Shanghai Key Laboratory of Visual Impairment and Restoration (Fudan University) Shanghai 200031 China. Electronic address: xhsun@***.

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Changes and Quantitative Characterization of Hyper-Viscoelastic Biomechanical Properties for Young Corneal Stroma after Standard Corneal Cross-Linking Treatment with Different Ultraviolet-A Energies

SSRN

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SSRN 2020年

作者： Liu, Taiwei Shen, Min Li, Hongxun Zhang, Yan Mu, Bokun Zhao, Xinheng Wang, Yan Department of Mechanics School of Mechanical Engineering Tianjin University Tianjin300350 China Tianjin Key Lab of Ophthalmology and Visual Science Tianjin Eye Hospital Tianjin300020 China Clinical College of Ophthalmology Tianjin Medical University Tianjin300070 China Department of Engineering Mechanics School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China

Corneal collagen cross-linking (CXL) treatment can restore vision in patients suffering from keratoconus and corneal injury, by improving the mechanical properties of the cornea. The correlation between ultraviolet-A (UVA) irradiant energies of standard CXL (SCXL) and corneal visco-hyperelastic mechanical behavior remains unknown. In this study, SCXL with four different UVA irradiant energy doses (0-5.4 J/cm2) were administered as part of quantitative treatments of corneal stromal lenticules extracted from young myopic patients via small incision lenticule extraction (SMILE) corneal refractive surgery. Double-strip samples with symmetric geometries were cut simultaneously for SCXL treatment and non-treated control. First, 40 pairs of strips were loaded to failure to assess the mechanical parameters of the material. Then, another 40 pairs were tested using a special uniaxial tensile test including quasi-static loading-unloading, instantaneous loading, and stress relaxation, to determine the visco-hyperelastic mechanical behavior. Upon combining the collagen fibril crimping constitutive model with the quasi-linear viscoelastic model, it was observed that with increasing UVA energy dose, the corneal strength and hyperelastic stiffness were significantly enhanced, while the toughness and viscosity of the cornea were significantly reduced. Considering the quantitative analysis of SCXL and the rehabilitation prediction of keratoconus treatment, the results clarify the biomechanical behavior of human corneal stroma in SCXL clinical surgery. © 2020, The Authors. All rights reserved.

关键词： Viscoelasticity

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Strategies to control myopia in children:a review of the findings from the Anyang Childhood Eye Study

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Annals of Eye science 2018年第1期3卷 219-225页

作者： Shi-Ming Li Ningli Wang the Anyang Childhood Eye Study Group Beijing Tongren Eye Center Beijing Tongren HospitalBeijing Ophthalmology&Visual Science Key LabBeijing Institute of OphthalmologyCapital Medical UniversityBeijing 100000China

Myopia in children remains a major public health problem worldwide,especially in some Asian countries such as China,Singapore and *** many interventions have been attempted,few has been proven to be effective in controlling onset and progression of myopia in *** factors,genetic susceptibility or ethnic differences can affect the efficacy of these ***,many questions remain unclear and even controversial for controlling *** has the biggest population with myopia,especially for children ***,it is of importance to present what achievements Chinese scientists have made in the field of myopia control in *** summarize the current findings on myopia control in children from the Anyang Childhood Eye Study,including epidemiological data,clinical trials,systematic reviews and meta-analyses,and compare them with studies in other countries to find potential clues for controlling myopia in children.

关键词： Children myopia time outdoors near work Chinese eye exercises spectacles

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10-year fundus tessellation progression and retinal vein occlusion

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International Journal of ophthalmology(English edition) 2018年第7期11卷 1192-1197页

作者： Yan-Ni Yan Ya-Xing Wang Yan Yang Liang Xu Jie Xu Qian Wang Jing-Yan Yang Wen-Jia Zhou Wen-Bin Wei Jost B.Jonas Beijing Tongren Eye Center Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Beijing Tongren Hospital Capital Medical University Beijing 100730 China Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical UniversityBeijing Ophthalmology & Visual Science Key Lab Beijing 100730 China Beijing Aier-Intech Eye Hospital Beijing 100730 China Department of Ophthalmology Medical Faculty Mannheim of the Ruprecht-Karls-University Seegartenklinik Heidelberg 68167 Germany

AIM： To access the 10-year fundus tessellation progression in patients with retinal vein occlusion. METHODS： The Beijing Eye Study 2001/2011 is a populationbased longitudinal study. The study participants underwent a detailed physical and ophthalmic examination. Degree of fundus tessellation was graded by using fundus photographs of the macula and optic disc. Progression of fundus tessellation was calculated by fundus tessellation degree of 2011 minus degree of 2001. Fundus photographs were used for assessment of retinal vein occlusion. RESULTS： The Beijing Eye Study included 4403 subjects in 2001, 3468 subjects was repeated in 2011. Assessment of retinal vein obstruction and fundus tessellation progression were available for 2462 subjects（71.0%）, with 66 subjects fulfilled the diagnosis of retinal vein occlusion. Of the 66 participants, 59 participants with unilateral branch retinal vein occlusion, 5 participants with unilateral central retinal vein occlusion, 1 participant with bilateral branch retinal vein occlusion, and 1 participant with branch retinal vein occlusion in one eye and central retinal vein occlusion in the other eye. Mean degree of peripapillary fundus tessellation progression were significantly higher in the whole retinal vein occlusion group（0.33±0.39, P〈0.001）, central retinal vein occlusion group（0.71±0.8, P=0.025） and branch retinal vein occlusion group（0.29±0.34, P=0.006） than the control group（0.20±0.26）. After adjustment for age, prevalence of tilted disc, change of best corrected visual acuity, axial length, progression of peripapillary fundus tessellation was associated with the presence of retinal vein occlusion（P=0.004; regression coefficient B, 0.094; 95%CI, 0.029, 0.158; standardized coefficient B, 0.056）. As a corollary, after adjusting for smoking duration, systolic blood pressure, anterior corneal curvature, prevalence of RVO was associated with more peripapillary fundus tessellation progression（P〈0.001; regression coefficient B： 1

关键词： fundus tessellation retinal vein occlusion Beijing Eye Study

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