In this paper we present algorithms to diagnosis Pathological Myopia (PM) and detection of retinal structures and lesions such asOptic Disc (OD), Fovea, Atrophy and Detachment. All these tasks were performed in fundus imaging from PM patients and they are requirements to participate in the Pathologic Myopia Challenge (PALM). The challenge was organized as a half day Challenge, a Satellite Event of The IEEE International Symposium on Biomedical Imaging in Venice Italy.Our method applies different Deep Learning techniques for each task. Transfer learning is applied in all tasks using Xception as the baseline model. Also, some key ideas of YOLO architecture are used in the Optic Disc segmentation algorithm pipeline. We have evaluated our model's performance according the challenge rules in terms of AUC-ROC, F1-Score, Mean Dice Score and Mean Euclidean Distance. For initial activities our method has shown satisfactory results.
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Baidu Inc.（百度公司）；Sun Yat-sen University（中山大学）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（阿根廷国家科学与技术研究理事会）；UNICEN（阿根廷国立中央大学）；Medical University of Vienna（维也纳医科大学）
Fundus2Globe: Generative AI-Driven 3D Digital Twins for Personalized Myopia Management
School of Optometry, The Hong Kong Polytechnic University（香港理工大学验光学院）；Swiss Federal Institute of Technology Lausanne (EPFL)（瑞士洛桑联邦理工学院）；Centre for Eye and Vision Research (CEVR)（眼与视觉研究中心）
Research Group Ophthalmology, KU Leuven（KU Leuven眼科研究组）；Ophthalmology Department, UZ Leuven（UZ Leuven眼科部）；ESAT-PSI, KU Leuven（KU Leuven ESAT-PSI）；Hasselt University（哈塞尔特大学）；VITO NV
This investigation reports on the results of convolutional neural networks developed for the recently introduced PathologicAL Myopia (PALM) dataset, which consists of 1200 fundus images. We propose a new Optic Nerve Head (ONH)-based prediction enhancement for the segmentation of atrophy and fovea. Models trained with 400 available training images achieved an AUC of 0.9867 for pathological myopia classification, and a Euclidean distance of 58.27 pixels on the fovea localization task, evaluated on a test set of 400 images. Dice and F1 metrics for semantic segmentation of lesions scored 0.9303 an…
We present a summary of transfer learning based methods for several challenging myopic fundus image analysis tasks including classification of pathological and non-pathological myopia,localisation of fovea,and segmentation of optic disc.By adapting existing popular deep learning architectures,our proposed methods have achieved 1st and 2nd place in several tasks at the Pathologic Myopia Challenge held at ISBI2019.
SpecF2M: A Spectral-Aware Multi-task Network Estimating Axial Length and Refractive Error from Pediatric Fundus Photographs
The Chinese University of Hong Kong（香港中文大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Capital Medical University（首都医科大学）；Zhengzhou Second Hospital（郑州市第二人民医院）
OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images
School of Data Science, Fudan University（复旦大学数据科学学院）；The Hong Kong Polytechnic University（香港理工大学）；Eye & ENT Hospital, Fudan University（复旦大学眼耳鼻喉科医院）；College of Business and Economics, Australian National University（澳大利亚国立大学商学与经济学院）
FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation
Northeastern University（东北大学）；University of Alberta（阿尔伯塔大学）；National University of Singapore（新加坡国立大学）；Institute of High Performance Computing（高性能计算研究所）
Inception Institute of Artificial Intelligence（人工智能起源研究所）
Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. However, due to the special optical beam of fundus imaging and structure of the retina, natural image enhancement methods cannot be utilized directly to address this. In this paper, we first analyze the ophthalmoscope imaging system and simulate a reliable degradation of major inf…
Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement
organization= Research Center for Translational Medicine, Medical Innovation Center；State Key Laboratory of Cardiology, Shanghai East Hospital , city= Shanghai , postcode= 200120 , country= China；organization= The Institute for Biomedical Engineering \& Nano Science, Tongji University School of Medicine , city= Shanghai , postcode= 200120 , country= China；organization= Shanghai Research Institute for Intelligent Autonomous Systems , city= Shanghai , postcode= 201210 , country= China；organization= Department of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University , city= Shanghai , postcode= 200000 , country= China；organization= Ophthalmology, Isra Postgraduate Institute of Ophthalmology , city= Karachi , postcode= , country= Pakistan；organization= The Eye Centre, South City Hospital , city= Karachi , postcode= , country= Pakistan；organization= Center for the Development of Laboratory Equipment, Pakistan Council of Scientific；Industrial Research , city= Karachi , postcode= , country= Pakistan
Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction
LaTIM UMR 1101, Inserm（法国国家健康与医学研究院LaTIM UMR 1101实验室）；Univ Bretagne Occidentale（西布列塔尼大学）；Evolucare Technologies（Evolucare科技公司）；University of Electronic Science and Technology of China（电子科技大学）；College of Computer Science, Sichuan University（四川大学计算机学院）；IMT Atlantique
Retinal vessel segmentation is a fundamental step in screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Robustness is one of the most critical requirements for practical utilization, since the test images may be captured using different fundus cameras, or be affected by various pathological changes. We investigate this problem from a data augmentation perspective, with the merits of no additional training data or inference time. In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random v…
Automatic segmentation of retina vessels plays a pivotal role in clinical diagnosis of prevalent eye diseases, such as, Diabetic Retinopathy or Age-related Macular Degeneration. Due to the complex construction of blood vessels, with drastically varying thicknesses, accurate vessel segmentation can be quite a challenging task. In this work we show that it is possible to achieve near state-of-the-art performance, by crafting a careful thought pre-processing pipeline, without having to resort to complex networks and/or training routines. We also show that our model is able to maintain the same hi…
Beijing Institute of Technology（北京理工大学）；Chongqing Innovation Center, Beijing Institute of Technology（北京理工大学重庆创新中心）
Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis. Although many researches have been conducted on this task, most prior works paid too much attention to the designs of networks instead of considering the pathological association for lesions. Through investigating the pathogenic causes of DR lesions in advance, we found that certain lesions are closed to specific vessels and present relative patterns to each other. Motivated by the observation, we propose a relation transformer block (RTB) to incorporate attention mechanisms a…
