Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning.
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ABSTRACT:
SUBMITTER: Wang Z
PROVIDER: S-EPMC5945322 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
Publications
Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning.
Wang Zhuo Z Camino Acner A Hagag Ahmed M AM Wang Jie J Weleber Richard G RG Yang Paul P Pennesi Mark E ME Huang David D Li Dengwang D Jia Yali Y
Journal of biophotonics 20180209 5
Optical coherence tomography (OCT) can demonstrate early deterioration of the photoreceptor integrity caused by inherited retinal degeneration diseases (IRDs). A machine learning method based on random forests was developed to automatically detect continuous areas of preserved ellipsoid zone structure (an easily recognizable part of the photoreceptors on OCT) in 16 eyes of patients with choroideremia (a type of IRD). Pseudopodial extensions protruding from the preserved ellipsoid zone areas are ...[more]
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Project description:Diabetic macular edema (DME) and retina vein occlusion (RVO) are macular diseases in which central photoreceptors are affected due to pathological accumulation of fluid. Optical coherence tomography allows to visually assess and evaluate photoreceptor integrity, whose alteration has been observed as an important biomarker of both diseases. However, the manual quantification of this layered structure is challenging, tedious and time-consuming. In this paper we introduce a deep learning approach for automatically segmenting and characterising photoreceptor alteration. The photoreceptor layer is segmented using an ensemble of four different convolutional neural networks. En-face representations of the layer thickness are produced to characterize the photoreceptors. The pixel-wise standard deviation of the score maps produced by the individual models is also taken to indicate areas of photoreceptor abnormality or ambiguous results. Experimental results showed that our ensemble is able to produce results in pair with a human expert, outperforming each of its constitutive models. No statistically significant differences were observed between mean thickness estimates obtained from automated and manually generated annotations. Therefore, our model is able to reliable quantify photoreceptors, which can be used to improve prognosis and managment of macular diseases.
| S-EPMC7101374 | biostudies-literature
Project description:ImportanceNovel therapies for choroideremia, an X-linked recessive chorioretinal degeneration, demand a better understanding of the primary site(s) of cellular degeneration. Optical coherence tomography angiography allows for choriocapillaris (CC) imaging. We compared the extent of structural alterations of the CC, retinal pigment epithelium, and photoreceptors with multimodal imaging.ObservationsIn a clinical case series conducted from September 15, 2014, through February 5, 2015, 14 eyes of 7 male patients with choroideremia (median age, 34 years [interquartile range, 15-46 years]; age range, 13-48 years), 4 eyes of 2 women with choroideremia carrier state (both in mid-50s), and 6 eyes of 6 controls (median age, 42.5 years [interquartile range, 33-55 years]; age range, 24-55 years) underwent multimodal imaging with optical coherence tomography angiography and electroretinography. The mean (SD) macular CC density was 82.9% (13.4%) in patients with choroideremia, 93.0% (3.8%) in female carriers, and 98.2% (1.3%) in controls. The mean (SD) CC density in affected eyes was higher in regions with preserved (92.6% [5.8%]) vs absent (75.9% [12.6%]) ellipsoid zone (mean difference, 16.7%; 95% CI, 12.1% to 21.3%; P < .001). Seventeen of 18 eyes of the patients and carriers had outer retinal tubulations forming pseudopod-like extensions from islands of preserved ellipsoid zone. Outer retinal tubulations were associated with absence of underlying retinal pigment epithelium and were longer (r = -0.62; 95% CI, -0.84 to -0.19; P < .001) and more numerous (r = -0.71; 95% CI, -0.91 to -0.27; P < .001) in more severely affected eyes.Conclusions and relevanceThese findings suggest that regional changes in CC density correlate with photoreceptor structural alterations in choroideremia. Although closely coupled, the results suggest that retinal pigment epithelium loss is more extensive than photoreceptor loss.
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Project description:PurposeTo use a deep learning model to develop a fully automated method (fully semantic network and graph search [FS-GS]) of retinal segmentation for optical coherence tomography (OCT) images from patients with Stargardt disease.MethodsEighty-seven manually segmented (ground truth) OCT volume scan sets (5171 B-scans) from 22 patients with Stargardt disease were used for training, validation and testing of a novel retinal boundary detection approach (FS-GS) that combines a fully semantic deep learning segmentation method, which generates a per-pixel class prediction map with a graph-search method to extract retinal boundary positions. The performance was evaluated using the mean absolute boundary error and the differences in two clinical metrics (retinal thickness and volume) compared with the ground truth. The performance of a separate deep learning method and two publicly available software algorithms were also evaluated against the ground truth.ResultsFS-GS showed an excellent agreement with the ground truth, with a boundary mean absolute error of 0.23 and 1.12 pixels for the internal limiting membrane and the base of retinal pigment epithelium or Bruch's membrane, respectively. The mean difference in thickness and volume across the central 6 mm zone were 2.10 µm and 0.059 mm3. The performance of the proposed method was more accurate and consistent than the publicly available OCTExplorer and AURA tools.ConclusionsThe FS-GS method delivers good performance in segmentation of OCT images of pathologic retina in Stargardt disease.Translational relevanceDeep learning models can provide a robust method for retinal segmentation and support a high-throughput analysis pipeline for measuring retinal thickness and volume in Stargardt disease.
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Project description:Importance:Whether optical coherence tomography angiography (OCT-A) outperforms OCT to detect glaucoma remains inconclusive. Objective:To compare (1) the diagnostic performance for detection of glaucoma and (2) the structure-function association between inner macular vessel density and inner macular thickness. Design, Setting, and Participants:This cross-sectional study included 115 patients with glaucoma and 35 healthy individuals for measurements of retinal thickness and retinal vessel density, segmented between the anterior boundary of internal limiting membrane and the posterior boundary of the inner plexiform layer, over the 3 × 3-mm2 macula using swept-source OCT. All participants were Chinese. Visual sensitivity corresponding to the 3 × 3-mm2 macular region was expressed in unlogged 1/lambert for investigation of the structure-function associations. Diagnostic performance was evaluated with area under the receiver operating characteristic curves (AUCs). The study was conducted between January 12, 2016, and December 12, 2016. Main Outcomes and Measures:Area under the receiver operating characteristic curve and R2 analysis. Results:Of the 115 patients with glaucoma, 42 (36.5%) were women (mean [SD] age, 53.5 [13.4] years); of the 35 individuals with healthy eyes, 25 (71.4%) were women (age, 60.6 [5.9] years). Inner macular vessel density and thickness were 4.3% (95% CI, 2.4%-6.1%) and 21.1 μm (95% CI, 17.4-24.9 μm) smaller, respectively, in eyes with glaucoma compared with healthy eyes. The AUC of mean inner macular thickness for glaucoma detection was greater than that of mean inner macular vessel density (difference, 0.17; 95% CI, 0.01-0.31; P = .03). At 90% specificity, the sensitivity of mean inner macular thicknesses for detection of glaucoma was greater than that of mean inner macular vessel densities (difference, 29.2%; 95% CI, 11.5%-64.6%; P = .02). The strength of the structure-function association was stronger for mean inner macular thickness than mean inner macular vessel density in the linear (difference in R2 = 0.38; 95% CI, 0.22-0.54; P < .001) and nonlinear (difference in R2 = 0.36; 95% CI, 0.21-0.51; P < .001) regression models. Conclusions and Relevance:In this study, OCT measurement of inner macular thickness shows a higher diagnostic performance to detect glaucoma and a stronger structure-function association than the currently used OCT-A measurement of inner macular vessel density. These findings may suggest that OCT-A of the macula has a limited role in the diagnostic evaluation of glaucoma.
| S-EPMC6142951 | biostudies-literature
Project description:In macular spectral domain optical coherence tomography (SD-OCT) volumes, detection of the foveal center is required for accurate and reproducible follow-up studies, structure function correlation, and measurement grid positioning. However, disease can cause severe obscuring or deformation of the fovea, thus presenting a major challenge in automated detection. We propose a fully automated fovea detection algorithm to extract the fovea position in SD-OCT volumes of eyes with exudative maculopathy. The fovea is classified into 3 main appearances to both specify the detection algorithm used and reduce computational complexity. Based on foveal type classification, the fovea position is computed based on retinal nerve fiber layer thickness. Mean absolute distance between system and clinical expert annotated fovea positions from a dataset comprised of 240 SD-OCT volumes was 162.3?µm in cystoid macular edema and 262?µm in nAMD. The presented method has cross-vendor functionality, while demonstrating accurate and reliable performance close to typical expert interobserver agreement. The automatically detected fovea positions may be used as landmarks for intra- and cross-patient registration and to create a joint reference frame for extraction of spatiotemporal features in "big data." Furthermore, reliable analyses of retinal thickness, as well as retinal structure function correlation, may be facilitated.
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Project description:PurposeTo develop a fully automated algorithm for accurate detection of fovea location in atrophic age-related macular degeneration (AMD), based on spectral-domain optical coherence tomography (SD-OCT) scans.MethodsImage processing was conducted on a cohort of patients affected by geographic atrophy (GA). SD-OCT images (cube volume) from 55 eyes (51 patients) were extracted and processed with a layer segmentation algorithm to segment Ganglion Cell Layer (GCL) and Inner Plexiform Layer (IPL). Their en face thickness projection was convolved with a 2D Gaussian filter to find the global maximum, which corresponded to the detected fovea. The detection accuracy was evaluated by computing the distance between manual annotation and predicted location.ResultsThe mean total location error was 0.101±0.145mm; the mean error in horizontal and vertical en face axes was 0.064±0.140mm and 0.063±0.060mm, respectively. The mean error for foveal and extrafoveal retinal pigment epithelium and outer retinal atrophy (RORA) was 0.096±0.070mm and 0.107±0.212mm, respectively. Our method obtained a significantly smaller error than the fovea localization algorithm inbuilt in the OCT device (0.313±0.283mm, p <.001) or a method based on the thinnest central retinal thickness (0.843±1.221, p <.001). Significant outliers are depicted with the reliability score of the method.ConclusionDespite retinal anatomical alterations related to GA, the presented algorithm was able to detect the foveal location on SD-OCT cubes with high reliability. Such an algorithm could be useful for studying structural-functional correlations in atrophic AMD and could have further applications in different retinal pathologies.
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Project description:We aimed to assess the ability of deep learning (DL) and support vector machine (SVM) to detect a nonperfusion area (NPA) caused by retinal vein occlusion (RVO) with optical coherence tomography angiography (OCTA) images. The study included 322 OCTA images (normal: 148; NPA owing to RVO: 174 [128 branch RVO images and 46 central RVO images]). Training to construct the DL model using deep convolutional neural network (DNN) algorithms was provided using OCTA images. The SVM used a scikit-learn library with a radial basis function kernel. The area under the curve (AUC), sensitivity and specificity for detecting an NPA were examined. We compared the diagnostic ability (sensitivity, specificity and average required time) between the DNN, SVM and seven ophthalmologists. Heat maps were generated. With regard to the DNN, the mean AUC, sensitivity, specificity and average required time for distinguishing RVO OCTA images with an NPA from normal OCTA images were 0.986, 93.7%, 97.3% and 176.9 s, respectively. With regard to SVM, the mean AUC, sensitivity, and specificity were 0.880, 79.3%, and 81.1%, respectively. With regard to the seven ophthalmologists, the mean AUC, sensitivity, specificity and average required time were 0.962, 90.8%, 89.2%, and 700.6 s, respectively. The DNN focused on the foveal avascular zone and NPA in heat maps. The performance of the DNN was significantly better than that of SVM in all parameters (p < 0.01, all) and that of the ophthalmologists in AUC and specificity (p < 0.01, all). The combination of DL and OCTA images had high accuracy for the detection of an NPA, and it might be useful in clinical practice and retinal screening.
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Project description:Background and aimsSignificant macrophages infiltration in advanced atherosclerotic plaques promotes acute coronary events. Hence, the clinical imaging of macrophage content in coronary atherosclerotic plaques could potentially aid in identifying patients most at risk of future acute coronary events. The aim of this study was to introduce and validate a simple intravascular optical coherence tomography (IV-OCT) image processing method for automated, accurate and fast detection of macrophage infiltration within coronary atherosclerotic plaques.MethodsThis method calculates the ratio of the normalized-intensity standard deviation (NSD) values estimated over two axially-adjacent regions of interest in an IV-OCT cross-sectional image (B-scan). When applied to entire IV-OCT B-scans, this method highlights plaque areas with high NSD ratio values (NSDRatio), which was demonstrated to be correlated with the degree of coronary plaque macrophage infiltration.ResultsUsing an optimized NSDRatio threshold value, coronary plaque macrophage infiltration could be detected with ~88% sensitivity and specificity in a database of 28 IV-OCT scans from postmortem coronary segments. For comparison, using an optimized NSD threshold value, considered the standard IV-OCT signature for macrophages, coronary plaque macrophage infiltration could be detected with only ~55% sensitivity and specificity.ConclusionsThe proposed NSDRatio method significantly increases the sensitivity and specificity for the detection of coronary plaque macrophage infiltration compared to the standard NSD method. This computationally efficient method can be seamlessly implemented within standard IV-OCT imaging systems for in-vivo real-time imaging of macrophage content in coronary plaques, which could potentially aid in identifying patients most at risk of future acute coronary events.
| S-EPMC6878982 | biostudies-literature
Project description:A middle ear infection is a prevalent inflammatory disease most common in the pediatric population, and its financial burden remains substantial. Current diagnostic methods are highly subjective, relying on visual cues gathered by an otoscope. To address this shortcoming, optical coherence tomography (OCT) has been integrated into a handheld imaging probe. This system can non-invasively and quantitatively assess middle ear effusions and identify the presence of bacterial biofilms in the middle ear cavity during ear infections. Furthermore, the complete OCT system is housed in a standard briefcase to maximize its portability as a diagnostic device. Nonetheless, interpreting OCT images of the middle ear more often requires expertise in OCT as well as middle ear infections, making it difficult for an untrained user to operate the system as an accurate stand-alone diagnostic tool in clinical settings. Here, we present a briefcase OCT system implemented with a real-time machine learning platform for middle ear infections. A random forest-based classifier can categorize images based on the presence of middle ear effusions and biofilms. This study demonstrates that our briefcase OCT system coupled with machine learning can provide user-invariant classification results of middle ear conditions, which may greatly improve the utility of this technology for the diagnosis and management of middle ear infections.
| S-EPMC8147830 | biostudies-literature
Project description:PurposeWe proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net), to segment retinal fluid in diabetic macular edema (DME) in optical coherence tomography (OCT) volumes.MethodsThe 3- × 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc., Fremont, CA, USA) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and six healthy controls, age 61.3 ± 10.1 (mean ± SD), 33% female, and all DR cases were diagnosed as severe NPDR or PDR). 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 and testing ReF-Net. The effect of including OCTA data for retinal fluid segmentation was investigated in this study. Volumetric retinal fluid can be constructed using the output of ReF-Net. Area-under-receiver-operating-characteristic-curve, intersection-over-union (IoU), and F1-score were calculated to evaluate the performance of ReF-Net.ResultsReF-Net shows high accuracy (F1 = 0.864 ± 0.084) in retinal fluid segmentation. The performance can be further improved (F1 = 0.892 ± 0.038) by including information from both OCTA and structural OCT. ReF-Net also shows strong robustness to shadow artifacts. Volumetric retinal fluid can provide more comprehensive information than the two-dimensional (2D) area, whether cross-sectional or en face projections.ConclusionsA deep-learning-based method can accurately segment retinal fluid volumetrically on OCT/OCTA scans with strong robustness to shadow artifacts. OCTA data can improve retinal fluid segmentation. Volumetric representations of retinal fluid are superior to 2D projections.Translational relevanceUsing a deep learning method to segment retinal fluid volumetrically has the potential to improve the diagnostic accuracy of diabetic macular edema by OCT systems.
| S-EPMC7552937 | biostudies-literature