Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age / gender / eye / disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an ~10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.
Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences（中国科学院宁波工业技术研究院慈溪生物医学工程研究所）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Keck School of Medicine, University of Southern California（南加州大学凯克医学院）；First Affiliated Hospital of Wenzhou Medical University（温州医科大学附属第一医院）；Southern University of Science and Technology（南方科技大学）；University of Liverpool（利物浦大学）
Optical Coherence Tomography Angiography (OCT-A) is a non-invasive imaging technique, and has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of retinal vessels in OCT-A has been under-studied due to various challenges such as low capillary visibility and high vessel complexity, despite its significance in understanding many eye-related diseases. In addition, there is no publicly available OCT-A dataset with manually graded vessels for training and validation. To address these issues, for the first time in the field of reti…
Optical Coherence Tomography Angiography-OCTA dataset for the study of Diabetic Retinopathy
Symbiosis Institute of Technology, Symbiosis International (Deemed University)（共生国际（ deemed 大学）共生技术学院）；University of Technology Sydney（悉尼科技大学）；Natasha Eye Care（娜塔莎眼科护理中心）
Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review
Department of Mechanical and Electronic Engineering, Kunming University of Science and Technology（昆明理工大学机械电子工程系）；Eye Clinics, Yunnan First People's Hospital（云南第一人民医院眼科）
The University of Western Australia（西澳大利亚大学）；University of Surrey（萨里大学）；Academy of Athens（雅典科学院）；Surrey and Sussex Healthcare NHS Trust（萨里和萨塞克斯医疗保健 NHS 信托）；The University of Surrey（萨里大学）
Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentat…
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；School of Physics and Optoelectronic Engineering, Foshan University（佛山大学物理与光电工程学院）；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）
Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume. Methods: 3 x 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc.) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and 6 healthy controls). A CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training an…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensembl…
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；University of Liverpool（利物浦大学）；University of Southern California（南加州大学）；Southern University of Science and Technology（南方科技大学）；University of Chinese Academy of Sciences（中国科学院大学）
Optical Coherence Tomography Angiography (OCTA) has been increasingly used in the management of eye and systemic diseases in recent years. Manual or automatic analysis of blood vessel in 2D OCTA images (en face angiograms) is commonly used in clinical practice, however it may lose rich 3D spatial distribution information of blood vessels or capillaries that are useful for clinical decision-making. In this paper, we introduce a novel 3D vessel reconstruction framework based on the estimation of vessel depth maps from OCTA images. First, we design a network with structural constraints to predict…
Vidyasirimedhi Institute of Science & Technology（维迪亚西里梅迪科技学院）；Kamnoetvidya Science Academy（卡姆诺维迪亚科学学院）；Ningbo Institute of Industrial Technology, Chinese Academy of Sciences（中国科学院宁波工业技术研究院）；Cixi Institute of Biomedical Engineering（慈溪生物医学工程研究所）；Kasetsart University（泰国农业大学）；Nanyang Technological University（南洋理工大学）
While there have been increased researches using deep learning techniques for the extraction of vascular structure from the 2D en face OCTA, for such approach, it is known that the data annotation process on the curvilinear structure like the retinal vasculature is very costly and time consuming, albeit few tried to address the annotation problem. In this work, we propose the application of the scribble-base weakly-supervised learning method to automate the pixel-level annotation. The proposed method, called OCTAve, combines the weakly-supervised learning using scribble-annotated ground truth …
Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学Casey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）
VAMOS-OCTA: Vessel-Aware Multi-Axis Orthogonal Supervision for Inpainting Motion-Corrupted OCT Angiography Volumes
Department of Computer Science, Vanderbilt University（计算机科学系，范德比尔特大学）；Digital Technology and Innovation, Siemens Healthineers（数字技术与创新，西门子医疗）；Department of Biomedical Engineering, Vanderbilt University（生物医学工程系，范德比尔特大学）
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）
Optical coherence tomography angiography (OCTA) is an important imaging modality in many bioengineering tasks. The image quality of OCTA, however, is often degraded by Bulk Motion Artifacts (BMA), which are due to micromotion of subjects and typically appear as bright stripes surrounded by blurred areas. State-of-the-art methods usually treat BMA removal as a learning-based image inpainting problem, but require numerous training samples with nontrivial annotation. In addition, these methods discard the rich structural and appearance information carried in the BMA stripe region. To address thes…
Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所）；West China Hospital, Sichuan University（四川大学华西医院）；Edge Hill University（边山大学）；Institute of High-Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）
Deep Learning-assisted AMD Staging based on OCT and OCT Angiography
Casey Eye Institute, Oregon Health & Science University（奥勒冈健康与科学大学凯斯眼科研究所）；Department of Biomedical Engineering, Oregon Health & Science University（奥勒冈健康与科学大学生物医学工程系）；Department of Ophthalmology, Mackay Memorial Hospital（Mackay纪念医院眼科部）
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔朗根-纽伦堡分校）；Massachusetts Institute of Technology（麻省理工学院）；New England Eye Center（新英格兰眼科中心）；Tufts Medical Center（塔夫茨医疗中心）
We propose a novel method for non-rigid 3-D motion correction of orthogonally raster-scanned optical coherence tomography angiography volumes. This is the first approach that aligns predominantly axial structural features like retinal layers and transverse angiographic vascular features in a joint optimization. Combined with the use of orthogonal scans and favorization of kinematically more plausible displacements, the approach allows subpixel alignment and micrometer-scale distortion correction in all 3 dimensions. As no specific structures or layers are segmented, the approach is by design r…
University of Nevada, Reno（内华达大学里诺分校）；Deakin University（迪肯大学）；Houston Eye Associates（休斯顿眼科协会）；University of Nevada School of Medicine（内华达大学医学院）
Carrying out clinical diagnosis of retinal vascular degeneration using Fluorescein Angiography (FA) is a time consuming process and can pose significant adverse effects on the patient. Angiography requires insertion of a dye that may cause severe adverse effects and can even be fatal. Currently, there are no non-invasive systems capable of generating Fluorescein Angiography images. However, retinal fundus photography is a non-invasive imaging technique that can be completed in a few seconds. In order to eliminate the need for FA, we propose a conditional generative adversarial network (GAN) to…
University of Oxford（牛津大学）；Big Data Institute（大数据研究所）
Diabetic retinopathy (DR) is a complication of diabetes, and one of the major causes of vision impairment in the global population. As the early-stage manifestation of DR is usually very mild and hard to detect, an accurate diagnosis via eye-screening is clinically important to prevent vision loss at later stages. In this work, we propose an ensemble method to automatically grade DR using ultra-wide optical coherence tomography angiography (UW-OCTA) images available from Diabetic Retinopathy Analysis Challenge (DRAC) 2022. First, we adopt the state-of-the-art classification networks, i.e., Res…
Propagating Structural Guidance: Synthesizing Fluorescein Angiography from Fundus Images and Sparse OCT Scans
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education（教育部新一代人工智能技术及其跨学科应用重点实验室）；Tianyuan Honors School, Nanjing Medical University（南京医科大学天元荣誉学院）；Nanjing University of Science and Technology（南京理工大学）；Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院眼科）
University of Nevada, Reno（内华达大学雷诺分校）；Deakin University（迪肯大学）；Houston Eye Associates（休斯顿眼科协会）
Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters. FA also requires fluorescein dye that is injected intravenously, which might cause adverse effects ranging from nausea, vomiting to even fatal anaphylaxis. Currently, no other fast and non-invasive technique exists that can generate FA without coupling with Fundus photography. To eradicate the need for an invasive FA extraction procedure, we introduce an Attention-based Generative network that can synthesize Fluorescein Angiography from Fundus imag…
CITIC-Research Center of Information and Communication Technologies, University of A Coruña（科蒂克信息与通信技术研究中心，拉科鲁尼亚大学）；Department of Computer Science, University of A Coruña（拉科鲁尼亚大学计算机科学系）；University of A Coruña（拉科鲁尼亚大学）；CITIC-Research Center of Information and Communication Technologies（科蒂克信息与通信技术研究中心）
The analysis of different image modalities is frequently performed in ophthalmology as it provides complementary information for the diagnosis and follow-up of relevant diseases, like hypertension or diabetes. This work presents a hybrid method for the multimodal registration of color fundus retinography and fluorescein angiography. The proposed method combines a feature-based approach, using domain-specific landmarks, with an intensity-based approach that employs a domain-adapted similarity metric. The methodology is tested on a dataset of 59 image pairs containing both healthy and pathologic…
Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints
The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳港协同创新研究院）；Weizhi Meditech (Foshan) Co., Ltd（佛山伟智医疗科技有限公司）；Guangdong Weiren Meditech Co., Ltd（广东伟仁医疗科技有限公司）；South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；Shenzhen Eye Institute（深圳眼科研究院）；Shenzhen Eye Hospital（深圳眼科医院）；Jinan University（暨南大学）；Shaanxi Eye Hospital（陕西省眼科医院）；Xi’an People’s Hospital (Xi’an Fourth Hospital)（西安市人民医院（西安市第四医院））；Affiliated People’s Hospital of Northwest University（西北大学附属人民医院）
Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology（香港科技大学）；Shanghai Jiao Tong University Affiliated Sixth People’s Hospital（上海交通大学附属第六人民医院）；VUNO Inc.（VUNO公司）；Johannes Kepler University Linz（林茨约翰·开普勒大学）；Fudan University（复旦大学）；Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）
Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures perfusion of the retinal vasculature, to train an AI algorithm to generate vasculature maps from standard structural optical coherence tomography (OCT) images of the same retinae, both exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer perfusion of …
Deepwise AI Lab（深度智耀人工智能实验室）；Northwestern Polytechnical University（西北工业大学）；Peking University（北京大学）
Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases. Although being extensively studied, segmentation of blood vessels, particularly thin vessels and capillaries, remains challenging mainly due to the lack of an effective interaction between local and global features. In this paper, we propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans, respectively. In PC-Net, the pyramid …
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Massachusetts Institute of Technology（麻省理工学院）；Tufts School of Medicine（塔夫茨医学院）
Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature. Based on repeated optical coherence tomography (OCT) scans, intensity changes are observed over time and used to compute OCTA image data. OCTA data are prone to noise and artifacts caused by variations in flow speed and patient movement. We propose a novel iterative maximum a posteriori signal recovery algorithm in order to generate OCTA volumes with reduced noise and increased image quality. This algorithm is based on previous work on probabilistic…
University of Nevada, Reno（内华达大学雷诺分校）；Houston Eye Associates（休斯顿眼科协会）
In Fluorescein Angiography (FA), an exogenous dye is injected in the bloodstream to image the vascular structure of the retina. The injected dye can cause adverse reactions such as nausea, vomiting, anaphylactic shock, and even death. In contrast, color fundus imaging is a non-invasive technique used for photographing the retina but does not have sufficient fidelity for capturing its vascular structure. The only non-invasive method for capturing retinal vasculature is optical coherence tomography-angiography (OCTA). However, OCTA equipment is quite expensive, and stable imaging is limited to s…
University of Isfahan（伊斯法罕大学）；Isfahan University of Medical Sciences（伊斯法罕医科大学）
Diabetic Retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. It has been observed that MAs are more common in the inner retinal layers compared to the outer retinal layers in eyes suffering from DR. Optical Coherence Tomography (OCT) is a noninvasive imaging technique that provides a cross-sectional view of the retina and it has been used in recent years to diagnose many eye diseases. As …
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）
Retinal optical coherence tomography (OCT) and OCT angiography (OCTA) suffer from the degeneration of image quality due to speckle noise and bulk-motion noise, respectively. Because the cross-sectional retina has distinct features in OCT and OCTA B-scans, existing digital filters that can denoise OCT efficiently are unable to handle the bulk-motion noise in OCTA. In this Letter, we propose a universal digital filtering approach that is capable of minimizing both types of noise. Considering the retinal capillaries in OCTA are hard to differentiate in B-scans while having distinct curvilinear st…
Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images
Universitat Politècnica de Catalunya (UPC)（加泰罗尼亚理工大学）；Institut Clínic d´Oftalmología (ICOF)（眼科临床研究所）；Hospital Clínic de Barcelona（巴塞罗那临床医院）；Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center（智能数据科学与人工智能研究中心）；August Pi i Sunyer Biomedical Research Institute (IDIBAPS)（奥古斯特·皮伊·苏涅尔生物医学研究所）；School of Medicine, Universitat de Barcelona（巴塞罗那大学医学院）
Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results; however, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality named local intensity fusion (LIF). LIF is a capillary-enhanced volume computed directly from the input OC…
Eye movements, blinking and other motion during the acquisition of optical coherence tomography (OCT) can lead to artifacts, when processed to OCT angiography (OCTA) images. Affected scans emerge as high intensity (white) or missing (black) regions, resulting in lost information. The aim of this research is to fill these gaps using a deep generative model for OCT to OCTA image translation relying on a single intact OCT scan. Therefore, a U-Net is trained to extract the angiographic information from OCT patches. At inference, a detection algorithm finds outlier OCTA scans based on their surroun…
OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心眼科学国家重点实验室）；Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science（广东省眼科与视觉科学重点实验室）
Pattern Recognition Lab, FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学模式识别实验室）；Department of Ophthalmology, Universitätsklinikum Erlangen（埃尔朗根大学医院眼科）
Ophthalmological imaging utilizes different imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography. Multiple images with different modalities or acquisition times are often analyzed for the diagnosis of retinal diseases. Automatically aligning the vessel structures in the images by means of multi-modal registration can support the ophthalmologists in their work. Our method uses a convolutional neural network to extract features of the vessel structure in multi-modal retinal images. We jointly train a keypoint detection an…
This study is to characterize reflectance profiles of retinal blood vessels in optical coherence tomography (OCT), and to validate these vascular features to guide artery-vein classification in OCT angiography (OCTA) of human retina. Depth-resolved OCT reveals unique features of retinal arteries and veins. Retinal arteries show hyper-reflective boundaries at both upper (inner side towards the vitreous) and lower (outer side towards the choroid) walls. In contrary, retinal veins reveal hyper-reflectivity at the upper boundary only. Uniform lumen intensity was observed in both small and large ar…
National Taiwan University（台湾大学）；Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）
Automated vascular segmentation on optical coherence tomography angiography (OCTA) is important for the quantitative analyses of retinal microvasculature in neuroretinal and systemic diseases. Despite recent improvements, artifacts continue to pose challenges in segmentation. Our study focused on removing the speckle noise artifact from OCTA images when performing segmentation. Speckle noise is common in OCTA and is particularly prominent over large non-perfusion areas. It may interfere with the proper assessment of retinal vasculature. In this study, we proposed a novel Supervision Vessel Seg…
Retinal vessel segmentation is a crucial step in diagnosing and screening various diseases, including diabetes, ophthalmologic diseases, and cardiovascular diseases. In this paper, we propose an effective and efficient method for vessel segmentation in color fundus images using encoder-decoder based octave convolution networks. Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions and octave transposed convolutions for learning multiple-spatial-frequency features, thus can better capture retinal vasculat…
Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation
Huazhong University of Science and Technology（华中科技大学）；Huawei Technologies Company Limited（华为技术有限公司）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院）
Sun Yat-sen University（中山大学）；Sichuan University（四川大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）
Optical Coherence Tomography Angiography (OCTA) is a non-invasive and non-contacting imaging technique providing visualization of microvasculature of retina and optic nerve head in human eyes in vivo. The adequate image quality of OCTA is the prerequisite for the subsequent quantification of retinal microvasculature. Traditionally, the image quality score based on signal strength is used for discriminating low quality. However, it is insufficient for identifying artefacts such as motion and off-centration, which rely specialized knowledge and need tedious and time-consuming manual identificati…
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
Nikookari Eye Hospital（尼库卡里眼科医院）；Tabriz University of Medical Sciences（大不里士医科大学）；Cardiovascular Research Center（心血管研究中心）；Research Center for Evidence‑Based Medicine（循证医学研究中心）；Iranian EBM Centre: A Joanna Briggs Institute Affiliated Group（伊朗循证医学中心：乔安娜·布里格斯研究所附属机构）；Tabriz Islamic Art University（大不里士伊斯兰艺术大学）；Institute for Intelligent Systems Research and Innovation (IISRI)（智能系统研究与创新研究所（IISRI））；Deakin University（迪肯大学）
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；the Second Affiliated Hospital of Zhejiang University（浙江大学医学院附属第二医院）；Ningbo First Hospital（宁波市第一医院）；Edge Hill University（边山大学）；Ningbo University of Technology（宁波工程学院）
Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: featur…
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）
Peking University（北京大学）；Peking University First Hospital（北京大学第一医院）；MGH/BWH Center for Clinical Data Science（麻省总医院/布莱根妇女医院临床数据科学中心）
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible. To solve this problem, we propose a knowledge transfer based shape-consistent generative adversarial network (SC-GAN), which is an annotation-free approach that uses the knowledge from publicly available annotated fundus dataset to segment coronary arteries. The proposed network is trained in an end-to-end fashion, generating and segmenting synthe…
Quasi-multimodal-based pathophysiological feature learning for retinal disease diagnosis
Tianjin University（天津大学）；Tianjin Key Laboratory of Ophthalmology and Visual Science（天津眼科学与视觉科学重点实验室）；Tianjin Eye Institute（天津眼科研究院）；Tianjin Eye Hospital（天津眼科医院）；Clinical College of Ophthalmology, Tianjin Medical University（天津医科大学临床医学院）；Department of Ophthalmology, The Second Affiliated Hospital of Nanchang University（南昌大学第二附属医院眼科部）；Nankai University Affiliated Eye Hospital（南开大学附属眼科医院）
TAPE: A two-stage parameter-efficient adaptation framework for foundation models in OCT-OCTA analysis
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases（传染病智能追踪与预测国家重点实验室）；Tianjin Key Laboratory of Intelligent Robotic（天津市智能机器人重点实验室）；Institute of Robotics and Automatic Information System, Nankai University（南开大学机器人与自动化信息系统研究所）；Institute of Intelligence Technology and Robotic Systems（智能技术与机器人系统研究所）；Shenzhen Research Institute of Nankai University（南开大学深圳研究院）
This study is to demonstrate deep learning for automated artery-vein (AV) classification in optical coherence tomography angiography (OCTA). The AV-Net, a fully convolutional network (FCN) based on modified U-shaped CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features. A transfer learning process is also integrated to compensate for the limitation of available da…
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；School of Medicine and Health, TUM University Hospital（医学与健康学院，塔尔博恩大学医院）；School of Computation, Information and Technology, TUM（计算、信息与技术学院，塔尔博恩大学）；Weill Cornell Medicine, Cornell University, New York City（韦尔·科恩医学中心，康奈尔大学，纽约市）
Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；University of Zurich（苏黎世大学）
Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostly rely on supervised training with voxel-level annotations, which are costly to obtain. In this work, we present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matc…
