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 ultrasound that provides a basis appropriate to the discrimination of blood flow from eye motion. A singular value decomposition (SVD) can be used as a simple, robust, and efficient way to separate the Doppler fluctuations of blood flow from those of strong spatial coherence such as eye motion. We show that the SVD outperforms the conventional Fourier based filter to reveal slower blood flow, and dramatically improves the ability of LDH to reveal vessels of smaller size or with a pathologically reduced blood flow.
Laser Doppler holography (LDH) is a full-field imaging technique that was recently used in the human eye to reveal blood flow contrasts in the retinal and choroidal vasculature non-invasively, and with high temporal resolution. We here demonstrate that the ability of LDH to perform quantitative flow measurements with high temporal resolution enables arteriovenous differentiation in the retina and choroid. In the retina, arteries and veins can be differentiated on the basis of their respective power Doppler waveforms. Choroidal arteries and veins can instead be discriminated by computing low an…
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…
The morphology of retinal blood vessels can indicate various diseases in the human body, and researchers have been working on automatic scanning and segmentation of retinal images to aid diagnosis. This project compares the performance of four neural network architectures in segmenting retinal images, using a combined dataset from different databases, namely the UNet, DR-VNet, UNet-ResNet and UNet-VGG.
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…
Cairo higher institute for engineering, computer sciences and management（开罗工程、计算机科学与管理高等学院）；Majmaah University（迈加马大学）；high institute of engineering Shock academy Cairo（开罗肖克学院工程高等学院）
Diabetic retinopathy is the basic reason for visual deficiency. This paper introduces a programmed strategy to identify and dispense with the blood vessels. The location of the blood vessels is the fundamental stride in the discovery of diabetic retinopathy because the blood vessels are the typical elements of the retinal picture. The location of the blood vessels can help the ophthalmologists to recognize the sicknesses prior and quicker. The blood vessels recognized and wiped out by utilizing Gobar filter on two freely accessible retinal databases which are STARE and DRIVE. The exactness of …
Optical coherence tomography angiography (OCTA) can visualize vasculature structures, but provides limited information about the blood flow speeds. Here, we present a second generation variable interscan time analysis (VISTA) OCTA, which evaluates a quantitative surrogate marker for blood flow speed in vasculature. At the capillary level, spatially compiled OCTA and a simple temporal autocorrelation model, ρ(τ) = exp(-ατ), were used to evaluate a temporal autocorrelation decay constant, α, as the blood flow speed marker. A 600 kHz A-scan rate swept-source provides short interscan time OCTA and…
The choroid is a highly vascularized tissues supplying the retinal pigment epithelium and photoreceptors. Its implication in retinal diseases is gaining increasing interest. However, investigating the anatomy and flow of the choroid remains challenging. Here we show that laser Doppler holography provides high contrast imaging of choroidal vessels in humans, with a spatial resolution comparable to state of the art indocyanine green angiography and optical coherence tomography. Additionally, laser Doppler holography contributes to sort out choroidal arteries and veins by using a power Doppler sp…
MMM University of Technology（MMM理工大学）；National Institute of Technology, Hamirpur（哈米尔布尔国家理工学院）
Retinal blood vessels structure contains information about diseases like obesity, diabetes, hypertension and glaucoma. This information is very useful in identification and treatment of these fatal diseases. To obtain this information, there is need to segment these retinal vessels. Many kernel based methods have been given for segmentation of retinal vessels but their kernels are not appropriate to vessel profile cause poor performance. To overcome this, a new and efficient kernel based matched filter approach has been proposed. The new matched filter is used to generate the matched filter re…
Fujian Agriculture and Forestry University（福建农林大学）；Digital Fujian Research Institute of Big Data for Agriculture and Forestry（数字福建农林大数据研究院）
Accurate segmentation of the blood vessels of the retina is an important step in clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make the blood vessel segmentation still very challenging. A novel U-shaped network named Multi-module Concatenation which is based on Atrous convolution and multi-kernel pooling is put forward to retinal vessels segmentation in this paper. The proposed network structure retains three layers the essential stru…
Automated segmentation of vascular map in retinal images endeavors a potential benefit in diagnostic procedure of different ocular diseases. In this paper, we suggest a new unsupervised retinal blood vessel segmentation approach using top-hat transformation, contrast-limited adaptive histogram equalization (CLAHE), and 2-D Gabor wavelet filters. Initially, retinal image is preprocessed using top-hat morphological transformation followed by CLAHE to enhance only the blood vessel pixels in the presence of exudates, optic disc, and fovea. Then, multiscale 2-D Gabor wavelet filters are applied on …
In recent years, several automatic segmentation methods have been proposed for blood vessels in retinal fundus images, ranging from using cheap and fast trainable filters to complicated neural networks and even deep learning. One example of a filted-based segmentation method is B-COSFIRE. In this approach the image filter is trained with example prototype patterns, to which the filter becomes selective by finding points in a Difference of Gaussian response on circles around the center with large intensity variation. In this paper we discuss and evaluate several of these vessel segmentation met…
The Computer-Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first, feature extraction is a fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus. The main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic mod…
Bioinformatics Institute（生物信息学研究所）；Universitat Rovira i Virgili（罗维拉-威尔吉利大学）
In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization. In the generator network of the adversarial framework, spatial pyramid pooling, kernel factorization and squeeze excitation block are employed to enhance the feature representation in spatial domain on different scales with reduced computational complexity. In turn, the discriminator network of the adversarial framework is formulated by combining convolutional layers with an additional squeeze excitation block to differe…
Siksha ‘O’ Anusandhan (Deemed to be University)（锡克沙奥阿努山丹大学（被认定为大学））；ITER, Siksha ‘O’ Anusandhan (Deemed to be University)（锡克沙奥阿努山丹大学（被认定为大学）ITER学院）；Center for the Internet of Things, ITER, Siksha ‘O’ Anusandhan (Deemed to be University)（锡克沙奥阿努山丹大学（被认定为大学）ITER学院物联网中心）
Extracting blood vessels from retinal fundus images plays a decisive role in diagnosing the progression in pertinent diseases. In medical image analysis, vessel extraction is a semantic binary segmentation problem, where blood vasculature needs to be extracted from the background. Here, we present an image enhancement technique based on the morphological preprocessing coupled with a scaled U-net architecture. Despite a relatively less number of trainable network parameters, the scaled version of U-net architecture provides better performance compare to other methods in the domain. We validated…
Early diagnosis of retinal diseases such as diabetic retinopathy has had the attention of many researchers. Deep learning through the introduction of convolutional neural networks has become a prominent solution for image-related tasks such as classification and segmentation. Most tasks in image classification are handled by deep CNNs pretrained and evaluated on imagenet dataset. However, these models do not always translate to the best result on other datasets. Devising a neural network manually from scratch based on heuristics may not lead to an optimal model as there are numerous hyperparam…
Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images
Ahsanullah University of Science and Technology（阿萨努拉科技大学）
University of Warwick（华威大学）；Griffith University（格里菲斯大学）；University of Adelaide（阿德莱德大学）；King’s College London（伦敦国王学院）
The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e.g., diabetic, hypertensive and solar retinopathies. Existing works often fail in predicting the abnormal areas, e.g, sudden brighter and darker areas and are inclined to predict a pixel to background due to the significant class imbalance, leading to high accuracy and specificity while low sensitivity. To that end, we propose a novel error attention refining network (ERA-Net) that is capable of learning and predicting the potential false predictions in a two-stage mann…
Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization
University of Ibadan（伊巴丹大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；Hertie Institute for AI in Brain Health（赫蒂脑健康人工智能研究所）；University of Tübingen（蒂宾根大学）；University of Bern（伯尔尼大学）；University College London（伦敦大学学院）；Tübingen AI Center（蒂宾根人工智能中心）
University of Calgary（卡尔加里大学）；Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）；University of Southern Denmark（南丹麦大学）
The vascular structure of blood vessels is important in diagnosing retinal conditions such as glaucoma and diabetic retinopathy. Accurate segmentation of these vessels can help in detecting retinal objects such as the optic disc and optic cup and hence determine if there are damages to these areas. Moreover, the structure of the vessels can help in diagnosing glaucoma. The rapid development of digital imaging and computer-vision techniques has increased the potential for developing approaches for segmenting retinal vessels. In this paper, we propose an approach for segmenting retinal vessels t…
Purpose: To demonstrate that retinal microvasculature per se is a reliable biomarker for Diabetic Retinopathy (DR) and, by extension, cardiovascular diseases. Methods: Deep Learning Convolutional Neural Networks (CNN) applied to color fundus images for semantic segmentation of the blood vessels and severity classification on both vascular and full images. Vessel reconstruction through harmonic descriptors is also used as a smoothing and de-noising tool. The mathematical background of the theory is also outlined. Results: For diabetic patients, at least 93.8% of DR No-Refer vs. Refer classifica…
The detection of retinal blood vessels, especially the changes of small vessel condition is the most important indicator to identify the vascular network of the human body. Existing techniques focused mainly on shape of the large vessels, which is not appropriate for the disconnected small and isolated vessels. Paying attention to the low contrast small blood vessel in fundus region, first time we proposed to combine graph based smoothing regularizer with the loss function in the U-net framework. The proposed regularizer treated the image as two graphs by calculating the graph laplacians on ve…
Huazhong University of Science and Technology（华中科技大学）；Laboratoire de Recherche et Développement de l’EPITA（EPITA研发实验室）；Wuhan University（武汉大学）
Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.g., the curvilinear structure is darker than the context) for a more robust representation. In consequence, the performance usually drops…
Optical coherence tomography angiography (OCTA) is an imaging technique that allows for non-invasive investigation of the microvasculature in the retina. OCTA uses laser light reflectance to measure moving blood cells. Hereby, it visualizes the blood flow in the retina and can be used for determining regions with more or less blood flow. OCTA images contain the capillary network together with larger blood vessels, and in this paper we propose a method that segments larger vessels, capillaries and background. The segmentation is obtained using a dictionary-based machine learning method that req…
We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training. Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results and can be implemented using a simple and efficient fully-convolutional network with a parameter count of less than 0.8M. Furthermore, we show that this framework - called VLight - avoids overfitting to specific training images and generalizes well across different datasets, which makes it highly sui…
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）
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…
Purpose: To evaluate the sensitivity of normalized blood flow index (NBFI) for detecting early diabetic retinopathy (DR). Methods: Optical coherence tomography angiography (OCTA) images of 30 eyes from 20 healthy controls, 21 eyes of diabetic patients with no DR (NoDR) and 26 eyes from 22 patients with mild non-proliferative DR (NPDR) were analyzed in this study. The OCTA images were centered on the fovea and covered a 6 mm x 6 mm area. Enface projections of the superficial vascular plexus (SVP) and the deep capillary plexus (DCP) were obtained for the quantitative OCTA feature analysis. Three…
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 …
Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms
Islamic Azad University（伊斯兰阿扎德大学）；University of Tehran（德黑兰大学）
MAF-Net: Multiple attention-guided fusion network for fundus vascular image segmentation
School of Electrical and Automation Engineering, East China Jiaotong University（华东交通大学电气与自动化工程学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）
Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Department of Medical Physics and Biomedical Engineering, UCL（伦敦大学学院医学物理与生物医学工程系）；NIHR Biomedical Research Centre at Moorfields Eye Hospital（莫菲尔德眼科医院NIHR生物医学研究中心）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（惠康/EPSRC介入与外科科学中心）；Research Center for Healthcare Data Science, Zhejiang Lab（之江实验室医疗健康数据科学研究中心）；UCL Respiratory, University College London（伦敦大学学院呼吸病学中心）；UCL Institute of Ophthalmology, University College London（伦敦大学学院眼科学研究所）
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-cla…
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 …
