Automated Detection of Vessel Abnormalities on Fluorescein Angiogram in Malarial Retinopathy.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhao Y
PROVIDER: S-EPMC4459173 | biostudies-other | 2015 Jun
REPOSITORIES: biostudies-other
Similar Datasets
Project description:The detection and assessment of leakage in retinal fluorescein angiogram images is important for the management of a wide range of retinal diseases. We have developed a framework that can automatically detect three types of leakage (large focal, punctate focal, and vessel segment leakage) and validated it on images from patients with malarial retinopathy. This framework comprises three steps: vessel segmentation, saliency feature generation and leakage detection. We tested the effectiveness of this framework by applying it to images from 20 patients with large focal leak, 10 patients with punctate focal leak, and 5,846 vessel segments from 10 patients with vessel leakage. The sensitivity in detecting large focal, punctate focal and vessel segment leakage are 95%, 82% and 81%, respectively, when compared to manual annotation by expert human observers. Our framework has the potential to become a powerful new tool for studying malarial retinopathy, and other conditions involving retinal leakage.
| S-EPMC4450752 | biostudies-other
Project description:PurposeTo investigate the correlation between quantifiable vessel density, computed in an automated fashion, from ultra-widefield fluorescein angiography (UWFFA) images from patients with proliferative diabetic retinopathy (PDR) with visual acuity and macular thickness.MethodsWe performed a secondary analysis of a prospective randomized controlled trial. We designed and trained an algorithm to automate retinal vessel detection from input UWFFA images. We then used our algorithm to study the correlation between baseline vessel density and best corrected visual acuity (BCVA) and CRT for patients in the RECOVERY study. Reliability of the algorithm was tested using the intraclass correlation (ICC). 42 patients from the Intravitreal Aflibercept for Retinal Non-Perfusion in Proliferative Diabetic Retinopathy (RECOVERY) trial who had both baseline UWFFA images and optical coherence tomography (OCT) data were included in our study. These patients had PDR without significant center-involving diabetic macular edema (central retinal thickness [CRT] ≤320μm).ResultsOur algorithm analyzed UWFFA images with a reliability measure (ICC) of 0.98. A positive correlation (r = 0.4071, p = 0.0075) was found between vessel density and BCVA. No correlation was found between vessel density and CRT.ConclusionsOur algorithm is capable of reliably quantifying vessel density in an automated fashion from baseline UWFFA images. We found a positive correlation between computed vessel density and BCVA in PDR patients without center-involving macular edema, but not CRT.Translational relevanceOur work is the first to offer an algorithm capable of quantifying vessel density in an automated fashion from UWFFA images, allowing us to work toward studying the relationship between retinal vascular changes and important clinical endpoints, including visual acuity, in ischemic eye diseases.
| S-EPMC7485882 | biostudies-literature
Project description:Although a lot of work has been done on optical coherence tomography and color images in order to detect and quantify diseases such as diabetic retinopathy, exudates, or neovascularizations, none of them is able to evaluate the diffusion of the neovascularizations in retinas. Our work has been to develop a tool that is able to quantify a neovascularization and the fluorescein leakage during an angiography. The proposed method has been developed following a clinical trial protocol; images are taken by a Spectralis (Heidelberg Engineering). Detections are done using a supervised classification using specific features. Images and their detected neovascularizations are then spatially matched by an image registration. We compute the expansion speed of the liquid that we call diffusion index. This last one specifies the state of the disease, permits indication of the activity of neovascularizations, and allows a follow-up of patients. The method proposed in this paper has been built to be robust, even with laser impacts, to compute a diffusion index.
| S-EPMC5689132 | biostudies-literature
Project description:While recent advances in deep learning have significantly advanced the state of the art for vessel detection in color fundus (CF) images, the success for detecting vessels in fluorescein angiography (FA) has been stymied due to the lack of labeled ground truth datasets. We propose a novel pipeline to detect retinal vessels in FA images using deep neural networks (DNNs) that reduces the effort required for generating labeled ground truth data by combining two key components: cross-modality transfer and human-in-the-loop learning. The cross-modality transfer exploits concurrently captured CF and fundus FA images. Binary vessels maps are first detected from CF images with a pre-trained neural network and then are geometrically registered with and transferred to FA images via robust parametric chamfer alignment to a preliminary FA vessel detection obtained with an unsupervised technique. Using the transferred vessels as initial ground truth labels for deep learning, the human-in-the-loop approach progressively improves the quality of the ground truth labeling by iterating between deep-learning and labeling. The approach significantly reduces manual labeling effort while increasing engagement. We highlight several important considerations for the proposed methodology and validate the performance on three datasets. Experimental results demonstrate that the proposed pipeline significantly reduces the annotation effort and the resulting deep learning methods outperform prior existing FA vessel detection methods by a significant margin. A new public dataset, RECOVERY-FA19, is introduced that includes high-resolution ultra-widefield images and accurately labeled ground truth binary vessel maps.
| S-EPMC7648732 | biostudies-literature
Project description:BackgroundMalaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria.MethodA method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives.ResultsThe image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites.ConclusionImage based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method provides a consistent and robust way of generating the parasite clearance curves.
| S-EPMC3254597 | biostudies-literature
Project description:PurposeBased on standard screening techniques, sickle retinopathy reportedly occurs in 10% of adolescents with sickle cell disease (SCD). We performed a prospective, observational clinical study to determine if ultra-widefield fluorescein angiography (UWFA), spectral-domain optical coherence tomography (SD-OCT), and optical coherence tomography angiography (OCT-A) detect more-frequent retinopathy in adolescents with SCD.DesignCross-sectional study.MethodsSetting: Institutional.SubjectsSixteen adolescents with SCD, aged 10-19 years (mean age 14.9 years), and 5 age-equivalent controls (mean age 17.4 years).Observation proceduresExaminations including acuity, standard slit-lamp biomicroscopy, UWFA, SD-OCT, and OCT-A were performed.Main outcome measuresSickle retinopathy defined by biomicroscopic changes, Goldberg stages I-V, Penman scale, flow void on OCT-A, or macular thinning on SD-OCT.ResultsWhile 22 of 32 SCD eyes (68.8%) had retinopathy on biomicroscopy, by UWFA 4 of 24 (16.7%) SCD eyes had peripheral arterial occlusion (Goldberg I), and 20 of 24 eyes (83.3%) had peripheral arteriovenous anastomoses (Goldberg II) in addition. No patients had Goldberg stages III-V. By SD-OCT and OCT-A, thinning of the macula and flow voids in both the superficial and deep retinal capillary plexus were found in 6 of 30 (20%) eyes.ConclusionsAll 24 eyes with adequate UWFA studies demonstrated sickle retinopathy. SD-OCT and OCT-A, which have not been previously reported in the adolescent population, detected abnormal macular thinning and flow abnormalities undetected by biomicroscopy. These findings suggest that pediatric sickle retinopathy may be more prevalent than previously suspected. If these findings are confirmed with larger cross-sectional and prospective analyses, these approaches may enhance early screening for sickle retinopathy.
| S-EPMC5660939 | biostudies-literature
Project description:Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.
| S-EPMC4452786 | biostudies-literature
Project description:Automated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of annotations, which is the fundamental motivation of this work. In this study, we construct a dataset containing 1219 fundus images (from DR patients and healthy controls) with annotations of exudate lesions. In addition to exudate annotations, we also provide four additional labels for each image: left-versus-right eye label, DR grade (severity scale) from three different grading protocols, the bounding box of the optic disc (OD), and fovea location. This dataset provides a great opportunity to analyze the accuracy and reliability of different exudate detection, OD detection, fovea localization, and DR classification algorithms. Moreover, it will facilitate the development of such algorithms in the realm of supervised and semi-supervised learning.
| S-EPMC7679367 | biostudies-literature
Project description:Retinopathy provides a window into the underlying pathology of life-threatening malarial coma ("cerebral malaria"), allowing differentiation between 1) coma caused by sequestration of Plasmodium falciparum-infected erythrocytes in the brain and 2) coma with other underlying causes. Parasite sequestration in the brain is mediated by PfEMP1; a diverse parasite antigen that is inserted into the surface of infected erythrocytes and adheres to various host receptors. PfEMP1 sub-groups called "DC8" and "DC13" have been proposed to cause brain pathology through interactions with endothelial protein C receptor. To test this we profiled PfEMP1 gene expression in parasites from children with clinically defined cerebral malaria, who either had or did not have accompanying retinopathy. We found no evidence for an elevation of DC8 or DC13 PfEMP1 expression in children with retinopathy. However, the proportional expression of a broad subgroup of PfEMP1 called "group A" was elevated in retinopathy patients suggesting that these variants may play a role in the pathology of cerebral malaria. Interventions targeting group A PfEMP1 may be effective at reducing brain pathology.
| S-EPMC4677286 | biostudies-literature
Project description:Imaging arterial mechanical properties may improve vascular disease diagnosis. Pulse wave velocity (PWV) is a marker of arterial stiffness linked to cardio-vascular mortality. Pulse wave imaging (PWI) is a technique for imaging the pulse wave propagation at high spatial and temporal resolution. In this paper, we introduce adaptive PWI, a technique for the automated partition of heterogeneous arteries into individual segments characterized by most homogeneous pulse wave propagation, allowing for more robust PWV estimation. This technique was validated in a silicone phantom with a soft-stiff interface. The mean detection error of the interface was 4.67 ± 0.73 mm and 3.64 ± 0.14 mm in the stiff-to-soft and soft-to-stiff pulse wave transmission direction, respectively. This technique was tested in monitoring the progression of atherosclerosis in mouse aortas in vivo ( n = 11 ). The PWV was found to already increase at the early stage of 10 weeks of high-fat diet (3.17 ± 0.67 m/sec compared to baseline 2.55 ± 0.47 m/sec, ) and further increase after 20 weeks of high-fat diet (3.76±1.20 m/sec). The number of detected segments of the imaged aortas monotonically increased with the duration of high-fat diet indicating an increase in arterial wall property inhomogeneity. The performance of adaptive PWI was also tested in aneurysmal mouse aortas in vivo. Aneurysmal boundaries were detected with a mean error of 0.68±0.44 mm. Finally, initial feasibility was shown in the carotid arteries of healthy and atherosclerotic human subjects in vivo ( n = 3 each). Consequently, adaptive PWI was successful in detecting stiffness inhomogeneity at its early onset and monitoring atherosclerosis progression in vivo.
| S-EPMC6938555 | biostudies-literature