Deep learning has emerged as the predominant solution for classifying medical images. We intend to apply these developments to the ultra-widefield (UWF) retinal imaging dataset. Since UWF images can accurately diagnose various retina diseases, it is very important to clas sify them accurately and prevent them with early treatment. However, processing images manually is time-consuming and labor-intensive, and there are two challenges to automating this process. First, high perfor mance usually requires high computational resources. Artificial intelli gence medical technology is better suited for places with limited medical resources, but using high-performance processing units in such environ ments is challenging. Second, the problem of the accuracy of colour fun dus photography (CFP) methods. In general, the UWF method provides more information for retinal diagnosis than the CFP method, but most of the research has been conducted based on the CFP method. Thus, we demonstrate that these problems can be efficiently addressed in low performance units using methods such as strategic data augmentation and model ensembles, which balance performance and computational re sources while utilizing UWF images.
Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared to traditional fundus photography. Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities, or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection. We first improve on the s…
Robust Detection of Retinal Neovascularization in Widefield Optical Coherence Tomography
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；Department of Biomedical Engineering, Oregon Health & Science University（俄勒冈健康与科学大学生物医学工程系）；Department of Ophthalmology, Mackay Memorial Hospital（马偕纪念医院眼科）；Department of Ophthalmology, Aichi Medical University（爱知医科大学眼科）
Representation Transfer of Foundation Models for Ultra-Widefield Retinal Imaging
Institute of Ophthalmology, University College London（伦敦大学学院眼科研究所）；Wake Forest University School of Medicine（维克森林大学医学院）；Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences（弗吉尼亚理工大学-维克森林大学生物医学工程与科学学院）；University of Zagreb Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算机学院）；NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust（NIHR生物医学研究中心，摩尔菲尔兹眼科医院NHS基金会信托）
Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema
BiometricsAI, Universidad Autónoma de Madrid, Madrid, Spain（生物度量AI，马德里自治大学，马德里，西班牙）；Department of Mathematics, Universidad de Las Palmas de Gran Canaria, Spain（数学系，拉斯帕尔马斯大Canaria大学，西班牙）；HCTLab Research Group, Universidad Autónoma de Madrid, Madrid, Spain（HCTLab研究组，马德里自治大学，马德里，西班牙）
The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective
Naif Arab University for Security Sciences（奈伊夫阿拉伯安全科学大学）；Charles Sturt University（查尔斯特大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Medical University of Vienna（维也纳医科大学）；Carl Zeiss Meditec, Inc.（卡尔蔡司医疗技术公司）
The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize classifier training for optic nerve head detection and localization in optical coherence tomography en face images of the retina. We examine the effect of domain knowledge driven spatial complexity reduction on the resulting optic nerve head segmentation and localization performance. We present a machine learning approach for segmenting optic nerve head in 2D en face projections of 3D widefield swept source optical co…
UKRI CDT Biomedical AI（英国研究与创新署生物医学人工智能博士培养中心）；University of Edinburgh（爱丁堡大学）；School of Informatics（信息学院）；Usher Institute（厄舍研究所）
Deep learning (DL) models are very effective on many computer vision problems and increasingly used in critical applications. They are also inherently black box. A number of methods exist to generate image-wise explanations that allow practitioners to understand and verify model predictions for a given image. Beyond that, it would be desirable to validate that a DL model \textit{generally} works in a sensible way, i.e. consistent with domain knowledge and not relying on undesirable data artefacts. For this purpose, the model needs to be explained globally. In this work, we focus on image modal…
University of Glasgow（格拉斯哥大学）；Optos (Nikon)（Optos（尼康））
We propose a convolutional neural network for localising the centres of the optic disc (OD) and fovea in ultra-wide field of view scanning laser ophthalmoscope (UWFoV-SLO) images of the retina. Images captured in both reflectance and autofluorescence (AF) modes, and central pole and eyesteered gazes, were used. The method achieved an OD localisation accuracy of 99.4% within one OD radius, and fovea localisation accuracy of 99.1% within one OD radius on a test set comprising of 1790 images. The performance of fovea localisation in AF images was comparable to the variation between human annotato…
Automated surface segmentation is important and challenging in many medical image analysis applications. Recent deep learning based methods have been developed for various object segmentation tasks. Most of them are a classification based approach, e.g. U-net, which predicts the probability of being target object or background for each voxel. One problem of those methods is lacking of topology guarantee for segmented objects, and usually post processing is needed to infer the boundary surface of the object. In this paper, a novel model based on convolutional neural network (CNN) followed by a …
The Hong Kong Polytechnic University（香港理工大学）；Centre for Eye and Vision Research（眼科与视觉研究中心）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；Shantou University Medical College（汕头大学医学院）；The Second People's Hospital of Foshan（佛山市第二人民医院）；Affiliated Foshan Hospital of Southern Medical University（南方医科大学附属佛山医院）
National Heart and Lung Institute, Faculty of Medicine, Imperial College London（帝国理工学院医学院国家心肺研究所）；Department of Bioengineering, Faculty of Engineering, Imperial College London（帝国理工学院工程系生物工程系）；Royal Brompton Hospital（皇家布朗普顿医院）
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure …
Airdoc LLC（爱丁堡医疗科技有限公司）；Monash University（莫纳什大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）
For decades, advances in retinal imaging technology have enabled effective diagnosis and management of retinal disease using fundus cameras. Recently, ultra-wide-field (UWF) fundus imaging by Optos camera is gradually put into use because of its broader insights on fundus for some lesions that are not typically seen in traditional fundus images. Research on traditional fundus images is an active topic but studies on UWF fundus images are few. One of the most important reasons is that UWF fundus images are hard to obtain. In this paper, for the first time, we explore domain adaptation from the …
University of Cambridge（剑桥大学）；University of Salerno（萨莱诺大学）
Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art for most medical image segmentation tasks. Issues with class imbalance pose a significant challenge in medical datasets, with lesions often occupying a considerably smaller volume relative to the background. Loss functions used in the training of deep learning algorithms differ in their robustness to class imbalance, with direct consequences for model convergence. The most commonly used loss functions for segmentati…
Choroidal Vessel Segmentation on Indocyanine Green Angiography Images via Human-in-the-Loop Labeling
School of Optometry, The Hong Kong Polytechnic University（香港理工大学眼科视光学院）；Research Centre for SHARP Vision, The Hong Kong Polytechnic University（香港理工大学SHARP视觉研究中心）；Centre for Eye and Vision Research (CEVR)（眼科与视觉研究中心）；Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital（澳大利亚眼科研究中心，皇家维多利亚眼耳医院）；Department of Surgery (Ophthalmology), The University of Melbourne（墨尔本大学外科学系（眼科学））
Laser Doppler holography (LDH) is a full-field interferometric imaging technique recently applied in ophthalmology to measure blood flow, a parameter of high clinical interest. From the temporal fluctuations of digital holograms acquired at ultrafast frame rates, LDH reveals retinal and choroidal blood flow with a few milliseconds of temporal resolution. However LDH experiences difficulties to detect slower blood flow as it requires to work with low Doppler frequency shifts which are corrupted by eye motion. We here demonstrate the use of a spatio-temporal decomposition adapted from Doppler ul…
Laser Doppler holography is a planar blood flow imaging technique recently introduced in ophthalmology to image human retinal and choroidal blood flow non-invasively. Here we present a digital method based on the Doppler spectrum asymmetry that reveals the local direction of blood flow with respect to the optical axis in out-of-plane vessels. This directional information is overlaid on standard grayscale blood flow images to depict flow moving towards the camera in red and flow moving away from the camera in blue, as in ultrasound color Doppler imaging. We show that thanks to the strong contri…
Laser Doppler holography (LDH) is an interferometric blood flow imaging technique based on full-field measurements of the Doppler spectrum. LDH has so far been demonstrated in the retina with ultrafast cameras, typically at 75 kHz. We show here that a similar method can be implemented with camera frame rates 10 times slower than before. Thanks to energy conservation, low and high frequency local power Doppler signals have opposite variations, and a simple contrast inversion of the low frequency power Doppler reveals fast blood flow beyond the camera detection bandwidth for conventional laser D…
University of Iowa（爱荷华大学）；Beijing Institute of Ophthalmology（北京眼科研究所）；Beijing Tongren Hospital（北京同仁医院）；Capital University of Medical Science（首都医科大学）；Eye Hospital of China Academy of Chinese Medical Sciences（中国中医科学院眼科医院）；University of Notre Dame（圣母大学）；Heidelberg University（海德堡大学）
Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary and mutual influence between adjacent objects. The traditional graph-based optimal surface segmentation method has proven its effectiveness with its ability of capturing various surface priors in a uniform graph model. However, its efficacy heavily relies on handcrafted features that are used to define the surface cost for the "goodness" of a surface. Recently, deep learning (DL) is emerging as powerful tools for medical image segmentation thanks to its su…
Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images
Department of Applied Biology；Chemical Technology, The Hong Kong Polytechnic University；Department of Data Science \& AI, The Hong Kong Polytechnic University e2；School of Data Science, Fudan University e1；Department of Applied Mathematics, The Hong Kong Polytechnic University；Department of Biostatistics, City University of Hong Kong；Eye Institute；Department of Ophthalmology, Eye \& ENT Hospital, Fudan University；Eunice Kennedy Shriver National Institute of Child Health；College of Business；Economics, Australian National University
High-resolution retinal matrix imaging via computational motion and aberration correction
PSL University, ESPCI Paris, CNRS, Institut Langevin（巴黎文理研究大学，巴黎高等物理化工学院，法国国家科学研究中心，朗之万研究所）；Institute of Biomedical Optics, University of Lübeck（吕贝克大学生物医学光学研究所）；OWLO SAS（OWLO公司）；Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL)（北方气道研究中心，德国肺病研究中心成员）
Neural Étendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Display
Princeton University（普林斯顿大学）；Reality Labs Research, Meta（Meta 现实实验室研究部）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-scale Generation and Registration Enhancement
Hangzhou Dianzi University（杭州电子科技大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen University（深圳大学）；Misr Higher Institute for Commerce and Computers（米斯尔高等商务与计算机学院）
Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?
National University of Singapore（新加坡国立大学）；Northeastern University（东北大学）；University College London（伦敦大学学院）；NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust（莫菲尔德眼科医院 NHS 基金会信托 NIHR 生物医学研究中心）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡眼科研究所，新加坡国家眼科中心）；Institute of High-Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；The Chinese University of Hong Kong（香港中文大学）；Stadtspital Zürich（苏黎世市立医院）；Spross Research Institute（斯普罗斯研究所）；Semmelweis University（塞梅尔维斯大学）；Shantou University（汕头大学）；Shantou University Medical College（汕头大学医学院）
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）
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Electronic Science and Technology of China（电子科技大学）；West China Hospital, Sichuan University（四川大学华西医院）；Chengdu First People’s Hospital（成都市第一人民医院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；The Hong Kong University of Science and Technology（香港科技大学）
