Automated detection of leakage in fluorescein angiography images with application to malarial retinopathy.
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ABSTRACT:
SUBMITTER: Zhao Y
PROVIDER: S-EPMC4450752 | biostudies-other | 2015 Jun
REPOSITORIES: biostudies-other
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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:The detection and assessment of intravascular filling defects is important, because they may represent a process central to cerebral malaria pathogenesis: neurovascular sequestration. We have developed and validated a framework that can automatically detect intravascular filling defects in fluorescein angiogram images. It first employs a state-of-the-art segmentation approach to extract the vessels from images and then divide them into individual segments by geometrical analysis. A feature vector based on the intensity and shape of saliency maps is generated to represent the level of abnormality of each vessel segment. An AdaBoost classifier with weighted cost coefficient is trained to classify the vessel segments into normal and abnormal categories. To demonstrate its effectiveness, we apply this framework to 6,358 vessel segments in images from 10 patients with malarial retinopathy. The test sensitivity, specificity, accuracy, and area under curve (AUC) are 74.7%, 73.5%, 74.1% and 74.2% respectively when compared to the reference standard of human expert manual annotations. This performance is comparable to the agreement that we find between human observers of intravascular filling defects. Our method will be a powerful new tool for studying malarial retinopathy.
| S-EPMC4459173 | biostudies-other
Project description:PurposeThe purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data.MethodsA weakly supervised learning-based method was proposed to detect fluorescein leakage without the need for manual annotation of leakage areas. To enhance the representation of the network, a residual attention module (RAM) was designed as the core component of the proposed generator. Moreover, class activation maps (CAMs) were used to define a novel anomaly mask loss to facilitate more accurate learning of leakage areas. In addition, sensitivity, specificity, accuracy, area under the curve (AUC), and dice coefficient (DC) were used to evaluate the performance of the methods.ResultsThe proposed method reached a sensitivity of 0.73 ± 0.04, a specificity of 0.97 ± 0.03, an accuracy of 0.95 ± 0.05, an AUC of 0.86 ± 0.04, and a DC of 0.87 ± 0.01 on the HRA data set; a sensitivity of 0.91 ± 0.02, a specificity of 0.97 ± 0.02, an accuracy of 0.96 ± 0.03, an AUC of 0.94 ± 0.02, and a DC of 0.85 ± 0.03 on Zhao's publicly available data set; and a sensitivity of 0.71 ± 0.04, a specificity of 0.99 ± 0.06, an accuracy of 0.87 ± 0.06, an AUC of 0.85 ± 0.02, and a DC of 0.78 ± 0.04 on Rabbani's publicly available data set.ConclusionsThe experimental results showed that the proposed method achieves better performance on fluorescence leakage detection and can detect one image within 1 second and thus has great potential value for clinical diagnosis and treatment of retina-related diseases, such as diabetic retinopathy and malarial retinopathy.Translational relevanceThe proposed weakly supervised learning-based method that automates the detection of fluorescence leakage can facilitate the assessment of retinal-related diseases.
| S-EPMC8934548 | biostudies-literature
Project description:Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathology in the retinal vasculature such as microaneurysms (MAs) and vascular leakage. Despite its potential value for diagnosis and disease surveillance, objective quantitative assessment of retinal pathology by UWFA is currently limited because it requires laborious manual segmentation by trained human graders. In this report, we describe a novel fully automated software platform, which segments MAs and leakage areas in native and dewarped UWFA images with retinal vascular disease. Comparison of the algorithm with human grader-generated gold standards demonstrated significant strong correlations for MA and leakage areas (intraclass correlation coefficient (ICC)=0.78-0.87?and ICC=0.70-0.86, respectively, p=2.1×10-7?to 3.5×10-10?and p=7.8×10-6?to 1.3×10-9, respectively). These results suggest the algorithm performs similarly to human graders in MA and leakage segmentation and may be of significant utility in clinical and research settings.
| S-EPMC5512291 | 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: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: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:BackgroundFundus fluorescein angiography (FA) can be used to diagnose fundus diseases by observing dynamic fluorescein changes that reflect vascular circulation in the fundus. As FA may pose a risk to patients, generative adversarial networks have been used to convert retinal fundus images into fluorescein angiography images. However, the available methods focus on generating FA images of a single phase, and the resolution of the generated FA images is low, being unsuitable for accurately diagnosing fundus diseases.MethodsWe propose a network that generates multi-frame high-resolution FA images. This network consists of a low-resolution GAN (LrGAN) and a high-resolution GAN (HrGAN), where LrGAN generates low-resolution and full-size FA images with global intensity information, HrGAN takes the FA images generated by LrGAN as input to generate multi-frame high-resolution FA patches. Finally, the FA patches are merged into full-size FA images.ResultsOur approach combines supervised and unsupervised learning methods and achieves better quantitative and qualitative results than using either method alone. Structural similarity (SSIM), normalized cross-correlation (NCC) and peak signal-to-noise ratio (PSNR) were used as quantitative metrics to evaluate the performance of the proposed method. The experimental results show that our method achieves better quantitative results with structural similarity of 0.7126, normalized cross-correlation of 0.6799, and peak signal-to-noise ratio of 15.77. In addition, ablation experiments also demonstrate that using a shared encoder and residual channel attention module in HrGAN is helpful for the generation of high-resolution images.ConclusionsOverall, our method has higher performance for generating retinal vessel details and leaky structures in multiple critical phases, showing a promising clinical diagnostic value.
| S-EPMC9945680 | biostudies-literature
Project description:Background: To analyze the distribution of manifest lesions of diabetic retinopathy (DR) by fundus fluorescein angiography (FFA) and color fundus photography (FP).Methods: A total of 566 eyes of 324 Chinese patients diagnosed with DR were included in this retrospective study. DR severity was graded by the international grading criterion. The distributions of microaneurysms (MA), intraretinal hemorrhages/exudates (He/Ex), intraretinal microvascular abnormality (IRMA), capillary nonperfusion areas (NPA), and neovascularization (NV) were estimated by multiple logistic regression analyse based on nine-field FFA and FP images.Results: In mild nonproliferative diabetic retinopathy (NPDR), the highest frequency of MA was found in the posterior pole (67.7%), followed by the inferior nasal (59.4%), and the nasal (55.4%) fields. In moderate NPDR, MA frequently distributed in the posterior pole (98.0%), nasal (97.0%), superior (96.0%), inferior nasal (94.9%), and inferior (92.9%) fields, whereas He/Ex were most prevalent in the posterior pole (69.7%). In severe NPDR and proliferative DR, IRMA, NPA, and NV were more frequent in the nasal field, particularly in the inferior nasal field (60.3, 38.7, and 76.0%, respectively). All lesions were more observed in the combined posterior pole, nasal, and inferior nasal fields than in the posterior pole or combined two fields in the early and severe stages of DR (P?<?0.05).Conclusions: The manifest lesions of DR were common in the nasal field besides the posterior pole in Chinese patients. A combined examination of the posterior pole, nasal, and inferior nasal mid-peripheral retina would help to detect different retinal lesions of DR.Trial registration: ClinicalTrial. gov, NCT03528720 . Registered 18 May 2018 - Retrospectively registered.
| S-EPMC7709243 | biostudies-literature
Project description:PurposeEvaluate association of retinal nonperfusion (NP) on ultrawide field (UWF) fluorescein angiography (FA) with diabetic retinopathy (DR) severity and predominantly peripheral lesions (PPL).MethodsMulticenter observational study, 652 eyes (361 participants) having nonproliferative DR (NPDR) without center-involved diabetic macular edema in at least one eye. Baseline 200° UWF-color and UWF-FA images were graded by a central reading center for color-PPL and FA-PPL, respectively. UWF-FA was graded for NP index within concentric zones: posterior pole (<10 mm from fovea), midperiphery (10-15 mm), and far periphery (>15 mm).ResultsBaseline Early Treatment Diabetic Retinopathy Study DR severity was 31.7% no DR/mild NPDR, 24.1% moderate NPDR, 14.0% moderately severe NPDR, 25.6% severe/very severe NPDR, and 4.6% proliferative DR. Worse DR severity was associated with increased NP index overall (P = 0.002), in the posterior pole (P < 0.001), midperiphery (P < 0.001), and far periphery (P = 0.03). On average, 29.6% of imaged retinal NP was in the posterior pole, 33.7% in midperiphery, and 36.7% in far periphery. Increased NP index was associated with FA-PPL (P < 0.001) but not with color-PPL (P = 0.65).ConclusionApproximately, 70% of NP in diabetic eyes is located outside the posterior pole. Increased NP is associated with the presence of FA-PPL, suggesting UWF-FA may better predict future DR worsening than UWF-color alone.
| S-EPMC9233028 | biostudies-literature