1. Articles from Fei Shi

    1-19 of 19
    1. DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images

      DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images

      Speckle is a major quality degrading factor in optical coherence tomography (OCT) images. In this work we propose a new deep learning network for speckle reduction in retinal OCT images, termed DeSpecNet. Unlike traditional algorithms, the model can learn from training data instead of manually selecting parameters such as noise level. The proposed deep convolutional neural network (CNN) applies strategies including residual learning, shortcut connection, batch normalization and leaky rectified linear units to achieve good despeckling performance. Application of the proposed method to the OCT images shows great improvement in both visual quality and quantitative indices. The proposed method provides ...

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    2. Retinal Optical Coherence Tomography Image Analysis (Textbook)

      Retinal Optical Coherence Tomography Image Analysis (Textbook)

      This book introduces the latest optical coherence tomography (OCT) imaging and computerized automatic image analysis techniques, and their applications in the diagnosis and treatment of retinal diseases. Discussing the basic principles and the clinical applications of OCT imaging, OCT image preprocessing, as well as the automatic detection and quantitative analysis of retinal anatomy and pathology, it includes a wealth of clinical OCT images, and state-of-the-art research that applies novel image processing, pattern recognition and machine learning methods to real clinical data. It is a valuable resource for researchers in both medical image processing and ophthalmic imaging.

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    3. A RESNET-BASED UNIVERSAL METHOD FOR SPECKLE REDUCTION IN OPTICAL COHERENCE TOMOGRAPHY IMAGES

      A RESNET-BASED UNIVERSAL METHOD FOR SPECKLE REDUCTION IN OPTICAL COHERENCE TOMOGRAPHY IMAGES

      In this work we propose a ResNet-based universal method for speckle reduction in optical coherence tomography (OCT) images. The proposed model contains 3 main modules: Convolution-BN-ReLU, Branch and Residual module. Unlike traditional algorithms, the model can learn from training data instead of selecting parameters manually such as noise level. Application of this proposed method to the OCT images shows a more than 22 dB signal-to-noise ratio improvement in speckle noise reduction with minimal structure blurring. The proposed method provides strong generalization ability and can process noisy other types of OCT images without retraining. It outperforms other filtering methods in suppressing ...

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    4. Nondestructive Measurement of Conformal Coating Thickness on Printed Circuit Board with Ultra-high Resolution Optical Coherence Tomography

      Nondestructive Measurement of Conformal Coating Thickness on Printed Circuit Board with Ultra-high Resolution Optical Coherence Tomography

      Conformal coating (CC) is widely used to protect printed circuit board (PCB) from corrosion, mould growth and electrical failures. To ensure an effective protection, the thickness of the conformal coating layer needs to be well controlled. However, to date, the coating thickness is usually measured in a destructive way under microscopes. In this study, we proposed to use optical coherence tomography (OCT) to measure the CC thickness nondestructively. Specifically, to obtain a good accuracy in thickness measurement, we constructed a spectral domain OCT (SD-OCT) with ultra-high axial resolution to image the CC layer in three dimensions, and developed an image ...

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    5. Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN

      Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN

      Speckle noise in optical coherence tomography (OCT) impairs both the visual quality and the performance of automatic analysis. Edge preservation is an important issue for speckle reduction. In this paper, we propose an end-to-end framework for simultaneous speckle reduction and contrast enhancement for retinal OCT images based on the conditional generative adversarial network (cGAN). The edge loss function is added to the final objective so that the model is sensitive to the edge-related details. We also propose a novel method for obtaining clean images for training from outputs of commercial OCT scanners. The results show that the overall denoising performance ...

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    6. Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

      Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

      Optic nerve head (ONH) is a crucial region for glaucoma detection and tracking based on spectral domain optical coherence tomography (SD-OCT) images. In this region, the existence of a “hole” structure makes retinal layer segmentation and analysis very challenging. To improve retinal layer segmentation, we propose a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints. With the proposed method, both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD-OCT images. An overall mean unsigned border positioning error of 7 ...

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    7. Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole

      Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole

      Cystoid macular edema (CME) and macular hole (MH) are the leading causes for visual loss in retinal diseases. The volume of the CMEs can be an accurate predictor for visual prognosis. This paper presents an automatic method to segment the CMEs from the abnormal retina with coexistence of MH in three-dimensional-optical coherence tomography images. The proposed framework consists of preprocessing and CMEs segmentation. The preprocessing part includes denoising, intraretinal layers segmentation and flattening, and MH and vessel silhouettes exclusion. In the CMEs segmentation, a three-step strategy is applied. First, an AdaBoost classifier trained with 57 features is employed to generate ...

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    8. Nonrigid registration of 3D longitudinal optical coherence tomography volumes with choroidal neovascularization

      Nonrigid registration of 3D longitudinal optical coherence tomography volumes with choroidal neovascularization

      In this paper, we propose a 3D registration method for retinal optical coherence tomography (OCT) volumes. The proposed method consists of five main steps: First, a projection image of the 3D OCT scan is created. Second, the vessel enhancement filter is applied on the projection image to detect vessel shadow. Third, landmark points are extracted based on both vessel positions and layer information. Fourth, the coherent point drift method is used to align retinal OCT volumes. Finally, a nonrigid B-spline-based registration method is applied to find the optimal transform to match the data. We applied this registration method on 15 ...

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    9. Graph search: active appearance model based automated segmentation of retinal layers for optic nerve head centered OCT images

      Graph search: active appearance model based automated segmentation of retinal layers for optic nerve head centered OCT images

      In this paper, a novel approach combining the active appearance model (AAM) and graph search is proposed to segment retinal layers for optic nerve head(ONH) centered optical coherence tomography(OCT) images. The method includes two parts: preprocessing and layer segmentation. During the preprocessing phase, images is first filtered for denoising, then the B-scans are flattened. During layer segmentation, the AAM is first used to obtain the coarse segmentation results. Then a multi-resolution GS–AAM algorithm is applied to further refine the results, in which AAM is efficiently integrated into the graph search segmentation process. The proposed method was tested ...

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    10. Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography

      Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography

      Speckle artifacts can strongly hamper quantitative analysis of optical coherence tomography (OCT), which is necessary to provide assessment of ocular disorders associated with vision loss. Here, we introduce a method for speckle reduction, which leverages from low-rank + sparsity decomposition (LRpSD) of the logarithm of intensity OCT images. In particular, we combine nonconvex regularization-based low-rank approximation of an original OCT image with a sparsity term that incorporates the speckle. State-of-the-art methods for LRpSD require a priori knowledge of a rank and approximate it with nuclear norm, which is not an accurate rank indicator. As opposed to that, the proposed method provides ...

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    11. A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT

      A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT

      Branch retinal artery occlusion (BRAO) is an ocular emergency which could lead to blindness. Quantitative analysis of BRAO region in the retina is very needed to assessment of the severity of retinal ischemia. In this paper, a fully automatic framework was proposed to classify and segment BRAO based on 3D spectral-domain optical coherence tomography (SD-OCT) images. To the best of our knowledge, this is the first automatic 3D BRAO segmentation framework. First, a support vector machine (SVM) based classifier is designed to differentiate BRAO into acute phase and chronic phase, and the two types are segmented separately. To segment BRAO ...

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      Mentions: Haoyu Chen
    12. Profile and Determinants of Retinal Optical Intensity in Normal Eyes with Spectral Domain Optical ...

      Profile and Determinants of Retinal Optical Intensity in Normal Eyes with Spectral Domain Optical ...

      Purpose To investigate the profile and determinants of retinal optical intensity in normal subjects using 3D spectral domain optical coherence tomography (SD OCT). Methods A total of 231 eyes from 231 healthy subjects ranging in age from 18 to 80 years were included and underwent a 3D OCT scan. Forty-four eyes were randomly chosen to be scanned by two operators for reproducibility analysis. Distribution of optical intensity of each layer and regions specified by the Early Treatment of Diabetic Retinopathy Study (ETDRS) were investigated by analyzing the OCT raw data with our automatic graph-based algorithm. Univariate and multivariate analyses were ...

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    13. Automated segmentation of serous pigment epithelium detachment in SD-OCT images

      Automated segmentation of serous pigment epithelium detachment in SD-OCT images

      Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorio-retinal disease processes, which can cause the loss of central vision. A 3-D method is proposed to automatically segment serous PED in SD-OCT images. The proposed method consists of five steps: first, a curvature anisotropic diffusion filter is applied to remove speckle noise. Second, the graph search method is applied for abnormal retinal layer segmentation associated with retinal pigment epithelium (RPE) deformation. During this process, Bruch’s membrane, which doesn’t show in the SD-OCT images, is estimated with the convex hull algorithm. Third, the foreground and background seeds ...

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    14. Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography for central retinal artery occlusion

      Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography for central retinal artery occlusion

      Optical coherence tomography (OCT) provides not only morphological information but also information about layer-specific optical intensities, which may represent the underlying tissue properties. The purpose of this study is to quantitatively investigate the optical intensity of each retinal layers in central retinal artery occlusion (CRAO). Twenty-nine CRAO cases at acute phase and 33 normal controls were included. Macula-centered 3D OCT images were segmented with a fully-automated Iowa Reference Algorithm into 10 layers. Layer-specific mean intensities were determined and compared between the patient and control groups using multiple regression analysis while adjusting for age and optical intensity of the entire region ...

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    15. Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images with Serous Pigment Epithelial Detachments

      Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images with Serous Pigment Epithelial Detachments

      Automated retinal layer segmentation of optical coherence tomography (OCT) images has been successful for normal eyes but becomes challenging for eyes with retinal diseases if the retinal morphology experiences critical changes. We pro-pose a method to automatically segment the retinal layers in 3 - D OCT data with serous retinal pigment epithelial detachments (PED), which is a prominent feature of many chorioretinal disease processes. The proposed framework consists of the following steps: fast denoising and B-scan alignment, multi-resolution graph search based surface detection, PED region detection and surface correction above the PED region. The proposed technique was evaluated on a dataset ...

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    16. An Automated Framework of Inner Segment/Outer Segment Defect Detection for Retinal SD-OCT Images

      An Automated Framework of Inner Segment/Outer  Segment Defect Detection for Retinal SD-OCT Images

      The integrity of inner segment/outer segment (IS/OS) has high correlation with lower visual acuity in patients suffering from blunt trauma. An automated 3D IS/OS defect detection method based on the SD-OCT images was proposed. First, 11 surfaces were automatically segmented using the multiscale 3D graph-search approach. Second, the sub-volumes between surface 7 and 8 containing IS/OS region around the fovea (diameter of 1mm) were extracted and flattened based on the segmented retinal pigment epithelium layer. Third, 5 kinds of texture based features were extracted for each voxel. A KNN classifier was trained and each voxel was ...

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    17. Comparison of Retinal Thickness Measurements of Normal Eyes between Topcon Algorithm and a Graph Based Algorithm

      Comparison of Retinal Thickness Measurements of  Normal Eyes between Topcon Algorithm and a Graph  Based Algorithm

      To assess the agreement between Topcon built-in algorithm and our developed graph based algorithm, the retinal thickness of 9-sectors on an Early Treatment of Diabetic Retinopathy Study(ETDRS) chart measurements for normal subjects was compared. A total of fifty eyes were enrolled in this study. The overall and sectoral thickness on ETDRS chart were calculated using Topcon built-in algorithm and our developed three-dimensional graph based algorithm. Correlation analysis and agreement analysis were performed between the commercial algorithm and our algorithm. A high degree of correlation was found between the results obtained from the two methods was from 0.856 to ...

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    18. Quantitative Analysis of Retinal Layer Optical Intensities on Three-Dimensional Optical Coherence Tomography

      Quantitative Analysis of Retinal Layer Optical Intensities on Three-Dimensional Optical Coherence Tomography

      Purpose. To investigate the optical intensities of all retinal layers on three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) in normal subjects using an automatic measurement. Methods. Forty normal subjects underwent Topcon 3D OCT-1000 macula-centered scan. The raw data were automatically segmented into 10 layers using the 3D graph search approach. Then the mean and standard deviation of intensities of each layer were calculated. The image quality index was given by the OCT software. Correlation analysis was performed between the optical intensities in each layer and image quality and subject's age. Results. The correlation of optical intensities was strong from ...

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    19. Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography

      Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography

      Purpose: To investigate the optical intensities of all retinal layers on 3D spectral domain optical coherence tomography (SD-OCT) in normal subjects using an automatic measurement. Methods: Forty normal subjects underwent Topcon 3D-OCT 1000 macula-centered scan. The raw data was automatically segmented into 10 layers using the 3D graph search approach. Then the mean and standard deviation of intensities of each layer were calculated. The image quality index was given by the OCT software. Correlation analysis was performed between the optical intensities in each layer with the image quality and subject's age. Results: The correlation of optical intensities was strong ...

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    1-19 of 19
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    1. (16 articles) Soochow University
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    Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography Quantitative Analysis of Retinal Layer Optical Intensities on Three-Dimensional Optical Coherence Tomography Comparison of Retinal Thickness Measurements of  Normal Eyes between Topcon Algorithm and a Graph  Based Algorithm An Automated Framework of Inner Segment/Outer  Segment Defect Detection for Retinal SD-OCT Images Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images with Serous Pigment Epithelial Detachments Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography for central retinal artery occlusion Automated segmentation of serous pigment epithelium detachment in SD-OCT images Profile and Determinants of Retinal Optical Intensity in Normal Eyes with Spectral Domain Optical ... Nonrigid registration of 3D longitudinal optical coherence tomography volumes with choroidal neovascularization Nondestructive Measurement of Conformal Coating Thickness on Printed Circuit Board with Ultra-high Resolution Optical Coherence Tomography Optical Coherence Tomography Angiography in Myopic Patients Quantification of retinal microvasculature and neurodegeneration changes in branch retinal vein occlusion after resolution of cystoid macular edema on optical coherence tomography angiography