Noise transfer for unsupervised domain adaptation of retinal OCT images
Technical University of Munich（慕尼黑工业大学）；Institute of AI for Health, Helmholtz Munich（亥姆霍兹慕尼黑中心健康人工智能研究所）；Munich School for Data Science（慕尼黑数据科学学院）；Institute of Computational Biology, Helmholtz Munich（亥姆霍兹慕尼黑中心计算生物学研究所）；Ludwig-Maximilians-University（路德维希-马克西米利安大学）
Valentin Koch, Olle Holmberg, Hannah Spitzer, Johannes Schiefelbein, Ben Asani, Michael Hafner, Fabian J Theis
Optical coherence tomography (OCT) imaging from different camera devices causes challenging domain shifts and can cause a severe drop in accuracy for machine learning models. In this work, we introduce a minimal noise adaptation method based on a singular value decomposition (SVDNA) to overcome the domain gap between target domains from three different device manufacturers in retinal OCT imaging. Our method utilizes the difference in noise structure to successfully bridge the domain gap between different OCT devices and transfer the style from unlabeled target domain images to source images for which manual annotations are available. We demonstrate how this method, despite its simplicity, compares or even outperforms state-of-the-art unsupervised domain adaptation methods for semantic segmentation on a public OCT dataset. SVDNA can be integrated with just a few lines of code into the augmentation pipeline of any network which is in contrast to many state-of-the-art domain adaptation methods which often need to change the underlying model architecture or train a separate style transfer model. The full code implementation for SVDNA is available at https://github.com/ValentinKoch/SVDNA.
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）
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…
Monash University（莫纳什大学）；Airdoc；NVIDIA AI Technology Center（英伟达人工智能技术中心）
Retinal vessel segmentation plays a key role in computer-aided screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Recently, deep learning-based retinal vessel segmentation algorithms have achieved remarkable performance. However, due to the domain shift problem, the performance of these algorithms often degrades when they are applied to new data that is different from the training data. Manually labeling new data for each test domain is often a time-consuming and laborious task. In this work, we explore unsupervised domain adaptation in retinal vessel segmen…
KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
Artificial Intelligence Department University of Isfahan（人工智能系伊斯法罕大学）；Labbafinejad Hospital Shahid Beheshti University of Medical Science（拉巴法尼贾医院谢赫·贝赫什提医学科学大学）；Department of Mechanical Engineering Isfahan University of Technology（机械工程系伊斯法罕技术大学）
International Multi-Centre Validation of Unsupervised Domain Adaptation for Precise Discrimination between Normal and Abnormal Retinal Development
The University of Leicester（莱斯特大学）；International Institute of Information Technology（国际信息技术学院）；University of Leicester（莱斯特大学）；University of Sheffield（谢菲尔德大学）；Copenhagen University Hospital, Rigshospitalet-Glostrup（哥本哈根大学医院，rigshospitalet-glostrup院区）；University of Copenhagen（哥本哈根大学）；Yonsei University College of Medicine（延世大学医学院）
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；Helmholtz Zentrum Muenchen（亥姆霍兹慕尼黑中心）；University of Innsbruck（因斯布鲁克大学）；University of Oxford（牛津大学）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔德眼科医院国民保健服务基金会信托）；Boehringer Ingelheim Limited（勃林格殷格翰有限公司）；University College London（伦敦大学学院）；Cairo University（开罗大学）；Watany Eye Hospital（瓦塔尼眼科医院）；Heidelberg Engineering GmbH（海德堡工程有限公司）；University of Zurich（苏黎世大学）
MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis
Christian Doppler Laboratory for Artificial Intelligence in Retina（人工智能视网膜实验室）；Institute of Artificial Intelligence（人工智能研究所）；Center for Medical Data Science（医学数据科学中心）；Medical University of Vienna（维也纳医学大学）；Comprehensive Center for AI in Medicine（医学人工智能综合中心）；OPTIMA Lab（OPTIMA实验室）；Department of Ophthalmology（眼科学系）
本文提出多模态基础模型 MIRAGE 及配套基准，联合处理视网膜 OCT 与 SLO 图像的分类和分割任务，并在与通用、专用模型的比较中展现优越性能。
Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Henan University of Chinese Medicine（河南中医药大学）
3D to 2D retinal vessel segmentation is a challenging problem in Optical Coherence Tomography Angiography (OCTA) images. Accurate retinal vessel segmentation is important for the diagnosis and prevention of ophthalmic diseases. However, making full use of the 3D data of OCTA volumes is a vital factor for obtaining satisfactory segmentation results. In this paper, we propose a Progressive Attention-Enhanced Network (PAENet) based on attention mechanisms to extract rich feature representation. Specifically, the framework consists of two main parts, the three-dimensional feature learning path and…
Nanjing University of Science and Technology（南京理工大学）；The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院）
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality that allows micron-level resolution to visualize the retinal microvasculature. The retinal vessel segmentation in OCTA images is still an open problem, and especially the thin and dense structure of the capillary plexus is an important challenge of this problem. In this work, we propose a novel image magnification network (IMN) for vessel segmentation in OCTA images. Contrary to the U-Net structure with a down-sampling encoder and up-sampling decoder, the proposed IMN adopts the design of up-sampling encod…
MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images
Hong Kong University of Science and Technology（香港科技大学）；Guangdong Weiren Meditech Co., Ltd.（广东伟仁医疗科技有限公司）；Yunnan United Vision Innovations Technology Co., Ltd.（云南联合视力创新科技有限公司）；Guangdong Hospital of Integrated Traditional Chinese and Western Medicine（广东省中西医结合医院）；Second People’s Hospital of Foshan（佛山市第二人民医院）
West Virginia University（西弗吉尼亚大学）；Rockefeller Neuroscience Institute（洛克菲勒神经科学研究所）；Lane Department of Computer Science and Electrical Engineering, West Virginia University（西弗吉尼亚大学莱恩计算机科学与电气工程学院）；Ophthalmology and Visual Sciences, West Virginia University（西弗吉尼亚大学眼科学与视觉科学系）
In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal layers in our collected and pre-processed OCT image dataset. At the pre-processing stage, we conduct super resolution and image processing techniques on the raw OCT scans to improve the quality of the raw images. For our search strategy, different primitive operations are suggested to find the down- & up-sampling cell blocks, and the binary gate method…
BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Images
University of Electronic Science and Technology of China（电子科技大学）；Southern Methodist University（南方法律大学）；University of Texas Southwestern Medical Center（德克萨斯西南医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Georgetown University（杰斐逊大学）；Sichuan Provincial People’s Hospital（四川省级人民医院）；University of Connecticut（康涅狄格大学）
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Ural Federal University Named after the First President of Russia B. N. Yeltsin（俄罗斯首任总统B.N.叶利钦命名的乌拉尔联邦大学）；Ophthalmosurgery Clinic "Professorskaya Plus"（“Professorskaya Plus”眼科诊所）；Ural State Medical University（乌拉尔国立医科大学）
Retinal Optical Coherence Tomography (OCT), a noninvasive cross-sectional scan of the eye with qualitative 3D visualization of the retinal anatomy is use to study the retinal structure and the presence of pathogens. The advent of the retinal OCT has transformed ophthalmology and it is currently paramount for the diagnosis, monitoring and treatment of many eye pathogens including Macular Edema which impairs vision severely or Glaucoma that can cause irreversible blindness. However the quality of retinal OCT images varies among device manufacturers. Deep Learning methods have had their success i…
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…
University of Oxford（牛津大学）；Big Data Institute, University of Oxford（牛津大学大数据研究所）；School of Engineering, Newcastle University（纽卡斯尔大学工程学院）；Neura Health Group；Newcastle Eye Centre, Royal Victoria Infirmary（纽卡斯尔皇家维多利亚医院纽卡斯尔眼科中心）；Newcastle University（纽卡斯尔大学）；Newcastle Upon Tyne, UK（泰恩河畔纽卡斯尔（英国））
Optical coherence tomography (OCT) is a commonly-used method of extracting high resolution retinal information. Moreover there is an increasing demand for the automated retinal layer segmentation which facilitates the retinal disease diagnosis. In this paper, we propose a novel multiprediction guided attention network (MPG-Net) for automated retinal layer segmentation in OCT images. The proposed method consists of two major steps to strengthen the discriminative power of a U-shape Fully convolutional network (FCN) for reliable automated segmentation. Firstly, the feature refinement module whic…
Khulna University of Engineering and Technology（库尔纳工程技术大学）
In medical science, the use of computer science in disease detection and diagnosis is gaining popularity. Previously, the detection of disease used to take a significant amount of time and was less reliable. Machine learning (ML) techniques employed in recent biomedical researches are making revolutionary changes by gaining higher accuracy with more concise timing. At present, it is even possible to automatically detect diseases from the scanned images with the help of ML. In this research, we have taken such an attempt to detect retinal diseases from optical coherence tomography (OCT) X-ray i…
FaraAI Company（法拉人工智能公司）；University of Tehran（德黑兰大学）
Optical coherence tomography (OCT) imaging is a well-known technology for visualizing retinal layers and helps ophthalmologists to detect possible diseases. Accurate and early diagnosis of common retinal diseases can prevent the patients from suffering critical damages to their vision. Computer-aided diagnosis (CAD) systems can significantly assist ophthalmologists in improving their examinations. This paper presents a new enhanced deep ensemble convolutional neural network for detecting retinal diseases from OCT images. Our model generates rich and multi-resolution features by employing the l…
OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation
Nanjing University of Science and Technology（南京理工大学）
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 / d…
The Pingry School（平格里学校）；Chinle Comprehensive Health Care Facility（钦利综合医疗中心）；University of Maryland, College Park（马里兰大学帕克分校）；NVIDIA（英伟达）
30 million Optical Coherence Tomography (OCT) imaging tests are issued annually to diagnose various retinal diseases, but accurate diagnosis of OCT scans requires trained eye care professionals who are still prone to making errors. With better systems for diagnosis, many cases of vision loss caused by retinal disease could be entirely avoided. In this work, we present ReLaX, a novel deep learning framework for explainable, accurate classification of retinal pathologies which achieves state-of-the-art accuracy. Furthermore, we emphasize producing both qualitative and quantitative explanations o…
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…
In medical imaging, there are clinically relevant segmentation tasks where the output mask is a projection to a subset of input image dimensions. In this work, we propose a novel convolutional neural network architecture that can effectively learn to produce a lower-dimensional segmentation mask than the input image. The network restores encoded representation only in a subset of input spatial dimensions and keeps the representation unchanged in the others. The newly proposed projective skip-connections allow linking the encoder and decoder in a UNet-like structure. We evaluated the proposed m…
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 (OCT) scanning is useful in detecting various retinal diseases. However, there are not enough ophthalmologists who can diagnose retinal OCT images in much of the world. To provide OCT screening inexpensively and extensively, an automated diagnosis system is indispensable. Although many machine learning techniques have been presented for assisting ophthalmologists in diagnosing retinal OCT images, there is no technique that can diagnose independently without relying on an ophthalmologist, i.e., there is no technique that does not overlook any anomaly, including unle…
University of Bern（伯尔尼大学）；University Hospital of Bern（伯尔尼大学医院）
Optical Coherence Tomography (OCT) is the primary imaging modality for detecting pathological biomarkers associated to retinal diseases such as Age-Related Macular Degeneration. In practice, clinical diagnosis and treatment strategies are closely linked to biomarkers visible in OCT volumes and the ability to identify these plays an important role in the development of ophthalmic pharmaceutical products. In this context, we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume. We do so by adding a bidir…
University of Isfahan（伊斯法罕大学）；Duke University（杜克大学）；Duke University Medical Center（杜克大学医学中心）
Optical coherence tomography (OCT) helps ophthalmologists assess macular edema, accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is necessary for OCT-guided treatment management, which relies on a precise image segmentation step. As manual analysis of retinal fluids is a time-consuming, subjective, and error-prone task, there is increasing demand for fast and robust automatic solutions. In this study, a new convolutional neural architecture named RetiFluidNet is proposed for multi-class retinal fluid segmentation. The model benefits from hierarchi…
University of Nottingham（诺丁汉大学）；Shenzhen University（深圳大学）；Wuhan University of Technology（武汉理工大学）；University of Leicester（莱斯特大学）
In this paper, we propose a novel retinal layer boundary model for segmentation of optical coherence tomography (OCT) images. The retinal layer boundary model consists of 9 open parametric contours representing the 9 retinal layers in OCT images. An intensity-based Mumford-Shah (MS) variational functional is first defined to evolve the retinal layer boundary model to segment the 9 layers simultaneously. By making use of the normals of open parametric contours, we construct equal sized adjacent narrowbands that are divided by each contour. Regional information in each narrowband can thus be int…
Simultaneous column-based deep learning progression analysis of atrophy associated with AMD in longitudinal OCT studies
School of Computer Science and Engineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学计算机科学与工程学院）；The Alexander Grass Center for Bioengineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学亚历山大·格拉斯生物工程中心）；Hadassah Medical Center（哈达萨医疗中心）
Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images
The Hong Kong University of Science and Technology（香港科技大学）；Guangdong Provincial Hospital of Integrated Traditional Chinese and Western Medicine（广东省中西医结合医院）；Guangdong Weiren Meditech Co., Ltd（广东唯仁医疗科技有限公司）
Predicting risk of cardiovascular disease using retinal OCT imaging
University of Leeds（利兹大学）；Research Institute for Signals, Systems and Computational Intelligence（信号、系统与计算智能研究所）；CONICET-UNL（阿根廷科学技术研究委员会-国立洛马大学）；University of Manchester（曼彻斯特大学）；Manchester University NHS Foundation Trust（曼彻斯特大学NHS基金会信托基金）；European Molecular Biology Laboratory（欧洲分子生物学实验室）；European Bioinformatics Institute (EMBL-EBI)（欧洲生物信息研究所）；NIHR Manchester Biomedical Research Centre（NIHR曼彻斯特生物医学研究中心）；Manchester Academic Health Science Centre（曼彻斯特学术健康科学中心）
提出基于 VAE 与随机森林的视网膜 OCT 自监督方法，结合临床数据预测心肌梗死或卒中风险，性能优于 QRISK3，并揭示脉络膜层为关键特征。
Medical image segmentation is an important task for computer aided diagnosis. Pixelwise manual annotations of large datasets require high expertise and is time consuming. Conventional data augmentations have limited benefit by not fully representing the underlying distribution of the training set, thus affecting model robustness when tested on images captured from different sources. Prior work leverages synthetic images for data augmentation ignoring the interleaved geometric relationship between different anatomical labels. We propose improvements over previous GAN-based medical image synthes…
University of Rostock（罗斯托克大学）；Rostock University Medical Center（罗斯托克大学医学中心）
Advances in optical coherence tomography (OCT) have enabled noninvasive imaging of substructures of the human retina with high spatial resolution. OCT examinations are now a standard procedure in clinics and an integral part of ophthalmic research. The interpretation of the OCT helps ophthalmologists understand the impact of various retinal and systemic diseases on the structure of the retina in a way not previously possible. In the early stages of retinal diseases, however, the identification and analysis of small and localized substructural changes in the retina remains a challenge. We prese…
3D imaging modalities are becoming increasingly popular and relevant in retinal imaging owing to their effectiveness in highlighting structures in sub-retinal layers. OCT is one such modality which has great importance in the context of analysis of cystoid structures in subretinal layers. Signal to noise ratio(SNR) of the images obtained from OCT is less and hence automated and accurate determination of cystoid structures from OCT is a challenging task. We propose an automated method for detecting/segmenting cysts in 3D OCT volumes. The proposed method is biologically inspired and fast aided b…
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations
Xiamen University（厦门大学）；National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康医疗大数据国家研究院）；Tencent YouTu Lab（腾讯优图实验室）；Southern University of Science and Technology（南方科技大学）
Khalifa University（哈利法大学）；National University of Sciences and Technology（国立科技大学）
Retinal lesions play a vital role in the accurate classification of retinal abnormalities. Many researchers have proposed deep lesion-aware screening systems that analyze and grade the progression of retinopathy. However, to the best of our knowledge, no literature exploits the tendency of these systems to generalize across multiple scanner specifications and multi-modal imagery. Towards this end, this paper presents a detailed evaluation of semantic segmentation, scene parsing and hybrid deep learning systems for extracting the retinal lesions such as intra-retinal fluid, sub-retinal fluid, h…
Indian Institute of Information Technology, Sri City（印度信息技术学院，斯里城）
Image to Image Translation (I2I) is a challenging computer vision problem used in numerous domains for multiple tasks. Recently, ophthalmology became one of the major fields where the application of I2I is increasing rapidly. One such application is the generation of synthetic retinal optical coherence tomographic (OCT) scans. Existing I2I methods require training of multiple models to translate images from normal scans to a specific pathology: limiting the use of these models due to their complexity. To address this issue, we propose an unsupervised multi-domain I2I network with pre-trained s…
Shahid Beheshti University（沙希德·贝赫什提大学）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）
Intra retinal fluids or Cysts are one of the important symptoms of macular pathologies that are efficiently visualized in OCT images. Automatic segmentation of these abnormalities has been widely investigated in medical image processing studies. In this paper, we propose a new U-Net-based approach for Intra retinal cyst segmentation across different vendors that improves some of the challenges faced by previous deep-based techniques. The proposed method has two main steps: 1- prior information embedding and input data adjustment, and 2- IRC segmentation model. In the first step, we inject the …
Quantitative Characterization of Retinal Features in Translated OCTA
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Atrium-Health Wake Forest Baptist（Atrium Health 维克森林浸信会医疗中心）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Stanford University School of Medicine（斯坦福大学医学院）
Early Alzheimer's Disease Detection from Retinal OCT Images: A UK Biobank Study
Department of Computer Engineering Isik University Istanbul, Turkey（计算机工程系伊斯肯大学）；Department of Artificial Intelligence and Data Engineering（人工智能与数据工程系）；Data Engineering Istanbul Technical University Istanbul, Turkey（数据工程系伊斯坦布尔技术大学）
Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Here, a sparse and low rank decomposition based method is used for speckle reduction in retinal OCT images. This technique works on input data that consists of several B-scans of the same location. The next step is the batch alignment of the images using a sparse and low-rank decom…
