Genetic variation affects morphological retinal phenotypes extracted from UK Biobank optical coherence tomography images.
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ABSTRACT:
SUBMITTER: Currant H
PROVIDER: S-EPMC8143408 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The human retina is a complex multi-layered tissue which offers a unique window into systemic health and disease. Optical coherence tomography (OCT) is widely used in eye care and allows the non-invasive, rapid capture of retinal measurements in exquisite detail. We conducted genome- and phenome-wide analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed phenome-wide association analyses, associating retinal thicknesses with 1,866 incident ICD-based conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association analyses, identifying inherited genetic markers which influence the retina, and replicated our associations among 6,313 individuals from the LIFE-Adult Study. And lastly, we performed comparative association of phenome- and genome- wide associations to identify putative causal links between systemic conditions, retinal layer thicknesses, and ocular disease. Independent associations with incident mortality were detected for photoreceptor thinning and ganglion cell complex thinning. Significant phenotypic associations were detected between retinal layer thinning and ocular, neuropsychiatric, cardiometabolic and pulmonary conditions. Genome-wide association of retinal layer thicknesses yielded 259 loci. Consistency between epidemiologic and genetic associations suggested putative causal links between thinning of the retinal nerve fiber layer with glaucoma, photoreceptor segment with AMD, as well as poor cardiometabolic and pulmonary function with PS thinning, among other findings. In conclusion, retinal layer thinning predicts risk of future ocular and systemic disease. Furthermore, systemic cardio-metabolic-pulmonary conditions promote retinal thinning. Retinal imaging biomarkers, integrated into electronic health records, may inform risk prediction and potential therapeutic strategies.
| S-EPMC10246137 | biostudies-literature
Project description:An algorithm for the simulation of two-dimensional spectral domain optical coherence tomography images based on Maxwell's equations is presented. A recently developed and modified time-harmonic numerical solution of Maxwell's equations is used to obtain scattered far fields for many wave numbers contained in the calculated spectrum. The interferometer setup with its lenses is included rigorously with Fresnel integrals and the Debye-Wolf integral. The implemented model is validated with an existing FDTD algorithm by comparing simulated tomograms of single and multiple cylindrical scatterers for perpendicular and parallel polarisation of the incident light. Tomograms are presented for different realisations of multiple cylindrical scatterers. Furthermore, simulated tomograms of a ziggurat-shaped scatterer and of dentin slabs, with varying scatterer concentrations, are investigated. It is shown that the tomograms do not represent the physical structures present within the sample.
| S-EPMC6704163 | biostudies-literature
Project description:We demonstrate the usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms. In specific, we will focus on retinal optical coherence tomography (OCT) reconstruction and propose a novel segmentation based reconstruction framework with sparse representation, termed segmentation based sparse reconstruction (SSR). The SSR method uses automatically segmented retinal layer information to construct layer-specific structural dictionaries. In addition, the SSR method efficiently exploits patch similarities within each segmented layer to enhance the reconstruction performance. Our experimental results on clinical-grade retinal OCT images demonstrate the effectiveness and efficiency of the proposed SSR method for both denoising and interpolation of OCT images.
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Project description:PurposeWe assessed the effect of melanin on the appearance of hyperreflective outer retinal bands in optical coherence tomography (OCT) images.MethodsA total of 23 normal subjects and 51 patients with albinism were imaged using the Bioptigen high-resolution spectral-domain OCT. In addition, three wild type, three albino (slc45a2b4/b4 ), and eight tyrosinase mosaic zebrafish were imaged with the hand-held Bioptigen Envisu R2200 OCT. To identify pigmented versus nonpigmented regions in the tyrosinase mosaic zebrafish, en face summed volume projections of the retinal pigment epithelium (RPE) were created from volume scans. Longitudinal reflectivity profiles were generated from B-scans to assess the width and maximum intensity of the RPE band in fish, or the presence of one or two RPE/Bruch's membrane (BrM) bands in humans.ResultsThe foveal RPE/BrM appeared as two bands in 71% of locations in patients with albinism and 45% of locations in normal subjects (P = 0.0003). Pigmented zebrafish retinas had significantly greater RPE reflectance, and pigmented regions of mosaic zebrafish also had significantly broader RPE bands than all other groups.ConclusionsThe hyperreflective outer retinal bands in OCT images are highly variable in appearance. We showed that melanin is a major contributor to the intensity and width of the RPE band on OCT. One should use caution in extrapolating findings from OCT images of one or even a few individuals to define the absolute anatomic correlates of the hyperreflective outer retinal bands in OCT images.Translational relevanceMelanin affects the appearance of the outer retinal bands in OCT images. Use of animal models may help dissect the anatomic correlates of the complex reflective signals in OCT retinal images.
| S-EPMC5381330 | biostudies-literature
Project description:BackgroundWhile deep learning has delivered promising results in the field of ophthalmology, the hurdle to complete a deep learning study is high. In this study, we aim to facilitate small scale model trainings by exploring the role of preprocessing to reduce computational burden and accelerate learning.MethodsA small subset of a previously published dataset containing optical coherence tomography images of choroidal neovascularization, drusen, diabetic macula edema, and normal macula was modified using Fourier transformation and bandpass filter, producing high frequency images, original images, and low frequency images. Each set of images was trained with the same model, and their performances were compared.ResultsCompared to that with the original image dataset, the model trained with the high frequency image dataset achieved an improved final performance and reached maximum performance much earlier (in fewer epochs). The model trained with low frequency images did not achieve a meaningful performance.ConclusionAppropriate preprocessing of training images can accelerate the training process and can potentially facilitate modeling using artificial intelligence when limited by sample size or computational power.
| S-EPMC10108538 | biostudies-literature
Project description:The current gold standard for clinical diagnosis of melanoma is excisional biopsy and histopathologic analysis. Approximately 15-30 benign lesions are biopsied to diagnose each melanoma. In addition, biopsies are invasive and result in pain, anxiety, scarring, and disfigurement of patients, which can add additional burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT), with its high-resolution and intermediate penetration depth, can potentially provide required diagnostic information noninvasively. Here, we present an image analysis algorithm, "optical properties extraction (OPE)," which improves the specificity and sensitivity of OCT by identifying unique optical radiomic signatures pertinent to melanoma detection. We evaluated the performance of the algorithm using several tissue-mimicking phantoms and then tested the OPE algorithm on 69 human subjects. Our data show that benign nevi and melanoma can be differentiated with 97% sensitivity and 98% specificity. These findings suggest that the adoption of OPE algorithm in the clinic can lead to improvements in melanoma diagnosis and patient experience. SIGNIFICANCE: This study describes a noninvasive, safe, simple-to-implement, and accurate method for the detection and differentiation of malignant melanoma versus benign nevi.
| S-EPMC6836720 | biostudies-literature
Project description:Speckle noise limits the diagnostic capabilities of optical coherence tomography (OCT) images, causing both a reduction in contrast and a less accurate assessment of the microstructural morphology of the tissue. We present a speckle-noise reduction method for OCT volumes that exploits the advantages of adaptive-noise wavelet thresholding with a wavelet compounding method applied to several frames acquired from consecutive positions. The method takes advantage of the wavelet representation of the speckle statistics, calculated properly from a homogeneous sample or a region of the noisy volume. The proposed method was first compared quantitatively with different state-of-the-art approaches by being applied to three different clinical dermatological OCT volumes with three different OCT settings. The method was also applied to a public retinal spectral-domain OCT dataset to demonstrate its applicability to different imaging modalities. The results based on four different metrics demonstrate that the proposed method achieved the best performance among the tested techniques in suppressing noise and preserving structural information. The proposed OCT denoising technique has the potential to adapt to different image OCT settings and noise environments and to improve image quality prior to clinical diagnosis based on visual assessment.
| S-EPMC8211087 | biostudies-literature
Project description:Alström syndrome is a multisystemic disorder characterized by cone-rod dystrophy, hearing loss, obesity, insulin resistance and hyperinsulinemia, type 2 diabetes mellitus, dilated cardiomyopathy, and progressive hepatic and renal dysfunction. The cone-rod retinal dystrophy usually develops within a few weeks after birth. The authors examined a young boy with Alström syndrome by means of microperimetry and spectral domain optical coherence tomography (OCT). Instead of the typical alterations observed in cone-rod dystrophies, the characteristics of the central foveal tissue suggest signs of retinal immaturity, with only a single layer of short, thick cones and rods and immature short outer segments. High-speed and high-resolution spectral domain OCT allowed a detailed analysis of retinal layers in a young patient with Alström syndrome for the first time.
| S-EPMC3015152 | biostudies-literature
Project description:PurposeTo benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera.MethodsA convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results.ResultsThe CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison.ConclusionThe proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders.
| S-EPMC6697318 | biostudies-literature
Project description:Sonification is the process of representing data as non-speech audio signals. In this manuscript, we describe the auditory presentation of OCT data and images. OCT acquisition rates frequently exceed our ability to visually analyze image-based data, and multi-sensory input may therefore facilitate rapid interpretation. This conversion will be especially valuable in time-sensitive surgical or diagnostic procedures. In these scenarios, auditory feedback can complement visual data without requiring the surgeon to constantly monitor the screen, or provide additional feedback in non-imaging procedures such as guided needle biopsies which use only axial-scan data. In this paper we present techniques to translate OCT data and images into sound based on the spatial and spatial frequency properties of the OCT data. Results obtained from parameter-mapped sonification of human adipose and tumor tissues are presented, indicating that audio feedback of OCT data may be useful for the interpretation of OCT images.
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