OCT-GAN: Single Step Shadow and Noise Removal from Optical Coherence Tomography Images of the Human Optic Nerve Head
Singapore Eye Research Institute（新加坡眼科研究所）；National University of Singapore（新加坡国立大学）；Beihang University（北京航空航天大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）
Haris Cheong, Sripad Krishna Devalla, Thanadet Chuangsuwanich, Tin A. Tun, Xiaofei Wang, Tin Aung, Leopold Schmetterer, Martin L. Buist, Craig Boote, Alexandre … 展开作者
Haris Cheong, Sripad Krishna Devalla, Thanadet Chuangsuwanich, Tin A. Tun, Xiaofei Wang, Tin Aung, Leopold Schmetterer, Martin L. Buist, Craig Boote, Alexandre H. Thiéry, Michaël J. A. Girard
Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within 10.4ms. Mean average gradient magnitude (AGM) for the proposed algorithm was 57.2% higher than current state-of-the-art, while mean peak signal to noise ratio (PSNR), contrast to noise ratio (CNR), and structural similarity index metric (SSIM) increased by 11.1%, 154% and 187% respectively compared to single-frame B-scans. Mean intralayer contrast (ILC) improvement for the retinal nerve fiber layer (RNFL), photoreceptor layer (PR) and retinal pigment epithelium (RPE) layers decreased from 0.362 \pm 0.133 to 0.142 \pm 0.102, 0.449 \pm 0.116 to 0.0904 \pm 0.0769, 0.381 \pm 0.100 to 0.0590 \pm 0.0451 respectively. The proposed algorithm reduces the necessity for long image acquisition times, minimizes expensive hardware requirements and reduces motion artifacts in OCT images.
National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学研究生院）；Nanyang Technological University（南洋理工大学）；Medical University of Vienna（维也纳医科大学）
Purpose: To remove retinal shadows from optical coherence tomography (OCT) images of the optic nerve head(ONH). Methods:2328 OCT images acquired through the center of the ONH using a Spectralis OCT machine for both eyes of 13 subjects were used to train a generative adversarial network (GAN) using a custom loss function. Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast: a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow) and compared to compensated images. This was computed in the Retinal Nerve Fiber Layer (R…
Imperial College London（帝国理工学院）；UCLA（加州大学洛杉矶分校）；Jules Stein Eye Institute（朱尔斯·斯坦眼科研究所）；California State University（加州州立大学）；David Geffen School of Medicine（大卫·格芬医学院）；Technische Universität München（慕尼黑工业大学）
The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict…
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…
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）
Optical Coherence Tomography (OCT) is an imaging modality that has been widely adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. It can be used to diagnose pathological conditions of the eye, and for developing pre-operative surgical plans. In contrast to the posterior retina, imaging the anterior tissue structures, such as the limbus and cornea, results in B-scans that exhibit increased speckle noise patterns and imaging artifacts. These artifacts, such as shadowing and specularity, pose a challenge during the analysis of the acquired volumes as they…
University of Coimbra（科英布拉大学）；Erasmus MC（伊拉斯姆斯大学医学中心）；KU Leuven（鲁汶大学）；University of Porto（波尔图大学）；Centro Hospitalar e Universitário São João（圣若昂大学医院中心）；University Hospitals UZ Leuven（鲁汶大学医院）
The optic nerve head represents the intraocular section of the optic nerve (ONH), which is prone to damage by intraocular pressure. The advent of optical coherence tomography (OCT) has enabled the evaluation of novel optic nerve head parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane opening minimum-rim-width, these seem to be promising optic nerve head parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these optical coherence tomography derived biomarkers are mostly extracted through manual segment…
Ophthalmic Engineering and Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore（新加坡国立大学工程学院生物医学工程系眼科工程与创新实验室）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡国立眼科中心新加坡眼科研究所）；Rajan Eye Care Hospital（拉詹眼科护理医院）
Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma. Although several deep learning (DL) techniques have been recently proposed for the automated extraction (segmentation) and quantification of these morphological changes, the device specific nature and the difficulty in preparing manual segmentations (training data) limit their clinical adoption. With several new manufacturers and next-generation OCT devices entering the market, t…
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；National University of Singapore（新加坡国立大学）；Aravind Eye Care Systems（阿拉温德眼科护理系统）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）
The optic nerve head (ONH) typically experiences complex neural- and connective-tissue structural changes with the development and progression of glaucoma, and monitoring these changes could be critical for improved diagnosis and prognosis in the glaucoma clinic. The gold-standard technique to assess structural changes of the ONH clinically is optical coherence tomography (OCT). However, OCT is limited to the measurement of a few hand-engineered parameters, such as the thickness of the retinal nerve fiber layer (RNFL), and has not yet been qualified as a stand-alone device for glaucoma diagnos…
National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Nanyang Technological University Singapore（新加坡南洋理工大学）；Medical University of Vienna（维也纳医科大学）；Institute of Molecular and Clinical Ophthalmology, Basel（巴塞尔分子与临床眼科研究所）；Abyss Processing Pte Ltd（深渊处理私人有限公司）
Purpose: (1) To develop a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field optical coherence tomography (OCT) scans; (2) To assess whether 3D macula or ONH structures (or the combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study was performed which included wide-field swept-source OCT scans from 319 glaucoma subjects and 298 non-glaucoma subjects. All scans were compensated to improve deep-tissue visibility. We developed a deep learning algorithm to automatically lab…
Shanghai Jiao Tong University（上海交通大学）；The University of Alabama（阿拉巴马大学）；Shanghai General Hospital（上海市第一人民医院）
Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. But they generate less satisfactory outcomes when dealing with larger missing regions and texture-rich structures. Emerging deep learning-based methods such as encoder-decoder networks have shown promising results in natural image inpainting tasks. However, they ty…
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学研究生医学院）；Institute for Molecular and Clinical Ophthalmology（分子与临床眼科研究所）；Rigshospitalet（丹麦国家医院）；University of Copenhagen（哥本哈根大学）；Yong Loo Lin School of Medicine（杨潞龄医学院）；National University of Singapore（新加坡国立大学）；Save Sight Institute（拯救视力研究所）；The University of Sydney（悉尼大学）
Purpose: (1) To develop a deep learning algorithm to identify major tissue structures of the optic nerve head (ONH) in 3D optical coherence tomography (OCT) scans; (2) to exploit such information to robustly differentiate among healthy, optic disc drusen (ODD), and papilledema ONHs. It was a cross-sectional comparative study with confirmed ODD (105 eyes), papilledema due to high intracranial pressure (51 eyes), and healthy controls (100 eyes). 3D scans of the ONHs were acquired using OCT, then processed to improve deep-tissue visibility. At first, a deep learning algorithm was developed using …
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学研究生医学院）；National University of Singapore（新加坡国立大学）；Massachusetts Institute of Technology（麻省理工学院）；Institute for Molecular and Clinical Ophthalmology, Basel（巴塞尔分子与临床眼科研究所）；Abyss Processing Pte Ltd（深渊处理私人有限公司）
$\mathbf{Purpose}$: To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH robustness from a single optical coherence tomography (OCT) scan of the ONH; (3) identify what critical three-dimensional (3D) structural features make a given ONH robust. $\mathbf{Design}$: Retrospective cross-sectional study. $\mathbf{Methods}$: 316 subjects had their ONHs imaged with OCT before and after acute intraocular pressure (IOP) elevation through ophthalmo-dynamometry. IOP-induced lamina-cribrosa deformations…
Impact of Optic Nerve Tortuosity, Globe Proptosis, and Size on Retinal Ganglion Cell Thickness Across General, Glaucoma, and Myopic Populations: Insights from the UK Biobank
A Hybrid Deep Learning Classification of Perimetric Glaucoma Using Peripapillary Nerve Fiber Layer Reflectance and Other OCT Parameters from Three Anatomy Regions
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；OHSU-PSU School of Public Health（俄勒冈健康与科学大学-波特兰州立大学公共卫生学院）；Bascom Palmer Eye Institute, University of Miami（迈阿密大学巴斯康帕尔默眼科研究所）；Doheny Eye Center and David Geffen School of Medicine at UCLA（加州大学洛杉矶分校多希尼眼科中心与大卫·格芬医学院）；Southern California Eye Institute（南加州眼科研究所）；Wills Eye Hospital（威尔斯眼科医院）
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；National University of Singapore（新加坡国立大学）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Vilnius University（维尔纽斯大学）；Vilnius University Hospital Santaros Klinikos（维尔纽斯大学圣罗克利什科斯医院）；Massachusetts Institute of Technology（麻省理工学院）；Institute for Molecular and Clinical Ophthalmology（分子与临床眼科研究所）
Purpose: The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. Optical coherence tomography (OCT) is the current gold standard to visualize and quantify these changes, however the resulting 3D deep-tissue information has not yet been fully exploited for the diagnosis and prognosis of glaucoma. To this end, we aimed: (1) To compare the performance of two relatively recent geometric deep learning techniques in diagnosing glaucoma from a single OCT scan of the ONH; and (2) To identify the 3D structural features of the ON…
The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomography (OCT) is advantageous in visualizing and quantifying the choroid in vivo. (1) The lower boundary of the choroid (choroid-sclera interface) in OCT is fuzzy, which makes the automatic segmentation difficult and inaccurate. (2) The visualization of the choroid is hindered by the vessel shadows from the superficial layers of the inner retina. In this paper, we propose to incorporate medical and imaging prior knowledge with deep learning to addres…
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…
Nowadays, glaucoma is the leading cause of blindness worldwide. We propose in this paper two different deep-learning-based approaches to address glaucoma detection just from raw circumpapillary OCT images. The first one is based on the development of convolutional neural networks (CNNs) trained from scratch. The second one lies in fine-tuning some of the most common state-of-the-art CNNs architectures. The experiments were performed on a private database composed of 93 glaucomatous and 156 normal B-scans around the optic nerve head of the retina, which were diagnosed by expert ophthalmologists…
3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia -- A Key to Improving Glaucoma Diagnosis in Myopic Populations
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工研究与技术联盟）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Emory University School of Medicine（埃默里大学医学院）；National University of Singapore（新加坡国立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory Empathetic AI for Health Institute（埃默里大学共情健康人工智能研究所）；Emory Eye Center（埃默里眼科中心）；Emory School of Medicine（埃默里医学院）；Emory Clinic（埃默里诊所）；Abyss Processing Pte Ltd（Abyss Processing私人有限公司）
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…
Shadow artifacts from retinal vessels hinder the development of quantitative choroidal biomarkers in Optical Coherence Tomography (OCT), which limits the clinical applications of the choroidal OCT to the measures of layer thickness. In this paper, we present a novel framework that could eliminate the retinal shadows and realize the quantification of choroidal vasculature. Different from existing methods, we convert the shadow elimination into an object removal task, which can be handled by image inpainting techniques. We adopt and finetune a two-stage generative inpainting network which connec…
Divyadrishti Imaging Laboratory, IIT Roorkee, Indian Institute of Technology Roorkee（IIT Roorkee 的 Divyadrishti 成像实验室，印度理工学院 Roorkee）
Image data set from a multi-spectral animal imaging system is used to address two issues: (a) registering the oscillation in optical coherence tomography (OCT) images due to mouse eye movement and (b) suppressing the shadow region under the thick vessels/structures. Several classical and AI-based algorithms in combination are tested for each task to see their compatibility with data from the combined animal imaging system. Hybridization of AI with optical flow followed by Homography transformation is shown to be working (correlation value>0.7) for registration. Resnet50 backbone is shown to be…
Matroid（Matroid 公司）；University of Maryland at Baltimore County（马里兰大学巴尔的摩县分校）；Byers Eye Institute, Stanford University（斯坦福大学拜尔斯眼科研究所）；The Chinese University of Hong Kong（香港中文大学）；Tilganga Institute of Ophthalmology（提尔甘加眼科研究所）；Narayana Nethralaya（纳拉亚纳眼科医院）；Stanford University（斯坦福大学）
We describe a new approach to automated Glaucoma detection in 3D Spectral Domain Optical Coherence Tomography (OCT) optic nerve scans. First, we gathered a unique and diverse multi-ethnic dataset of OCT scans consisting of glaucoma and non-glaucomatous cases obtained from four tertiary care eye hospitals located in four different countries. Using this longitudinal data, we achieved state-of-the-art results for automatically detecting Glaucoma from a single raw OCT using a 3D Deep Learning system. These results are close to human doctors in a variety of settings across heterogeneous datasets an…
Purpose: To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis. Methods: Participants were imaged with 4.5X4.5-mm volumetric disc scans using spectral-domain optical coherence tomography (OCT). The normalized NFL reflectance map was processed by an azimuthal filter to reduce directional reflectance bias due to variation of beam incidence angle. The peripapillary area of the map was divided into 160 superpixels. Average reflectance was the mean of superpixel reflectance. Low-reflectance superpixels were identified as those with NFL reflectance below the 5 percentile normative c…
One of the important parameters for the assessment of glaucoma is optic nerve head (ONH) evaluation, which usually involves depth estimation and subsequent optic disc and cup boundary extraction. Depth is usually obtained explicitly from imaging modalities like optical coherence tomography (OCT) and is very challenging to estimate depth from a single RGB image. To this end, we propose a novel method using adversarial network to predict depth map from a single image. The proposed depth estimation technique is trained and evaluated using individual retinal images from INSPIRE-stereo dataset. We …
In today's clinics, a cellular-resolution view of the cornea can be achieved only with an in vivo confocal microscope (IVCM) in contact with the eye. Here, we present a common-path Full-field/Spectral-domain OCT microscope (FF/SD OCT), which, for the first time, enables cell-detail imaging of the entire ocular surface (central and peripheral cornea, limbus, sclera, tear film) without contact and in real time. The device, which has been successfully tested in human subjects, is now ready for direct implementation in clinical research. Real-time performance is achieved through rapid axial eye tr…
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 Information Science and Engineering, Shandong Normal University（山东师范大学信息科学与工程学院）；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）
Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3x3- or 6x6-mm. Compared to 3x3-mm angiograms with proper sampling density, 6x6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6x6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3x3-mm and 6x6-mm angiograms from the same eyes. The reconstructed 6x6-mm…
Wellman Center for Photomedicine, Massachusetts General Hospital（马萨诸塞州总医院韦尔曼光医学中心）；Harvard Medical School（哈佛医学院）；Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Erasmus Medical Center（伊拉斯谟医学中心）
Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses and provide critical vessel parameters to optimize percutaneous intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) simultaneously provides high-resolution cross-sectional images of vascular structures while also revealing preponderant tissue components such as collagen and smooth muscle and thereby enhances plaque characterization. Automated interpretation of these features promises to facilitate the objective clinical investigation of the na…
We present a cascade deep neural network to segment retinal vessels in volumetric optical coherence tomography (OCT). Two types of knowledge are infused into the network for confining the searching regions. (1) Histology. The retinal vessels locate between the inner limiting membrane and the inner nuclear layer of human retina. (2) Imaging. The red blood cells inside the vessels scatter the OCT probe light forward and form projection shadows on the retinal pigment epithelium (RPE) layer, which is avascular thus perfect for localizing the retinal vessel in transverse plane. Qualitative and quan…
Case Western Reserve University（凯斯西储大学）；The Ohio State University（俄亥俄州立大学）；The Hashemite University（哈希姆大学）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医疗中心）；Galeazzi San’Ambrogio Hospital（加拉齐圣安布罗焦医院）；University of South Florida（南佛罗里达大学）
To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shift…
Background: Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily unde…
