1. Articles from Yankui Sun

    1-9 of 9
    1. Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images

      Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images

      In conventional retinal region detection methods for optical coherence tomography (OCT) images, many parameters need to be set manually, which is often detrimental to their generalizability. We present a scheme to detect retinal regions based on fully convolutional networks (FCN) for automatic diagnosis of abnormal maculae in OCT images. The FCN model is trained on 900 labeled age-related macular degeneration (AMD), diabetic macular edema (DME) and normal (NOR) OCT images. Its segmentation accuracy is validated and its effectiveness in recognizing abnormal maculae in OCT images is tested and compared with traditional methods, by using the spatial pyramid matching based on ...

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    2. Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

      Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

      Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves their performance on retinal optical coherence tomography (OCT) images. Deep features of pre-trained DNN are high-level features of natural images. These features harm the training of transfer learning. Our strategy is to remove some deep convolutional layers of the state-of-the-art pre-trained networks: GoogLeNet, ResNet and DenseNet. We try to find the optimized deep neural networks on small-scale and large-scale OCT datasets, respectively, in our ...

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    3. Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images

      Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images

      We present an automatic method based on transfer learning for the identification of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retinal optical coherence tomography (OCT) images. The algorithm aims to improve the classification performance of retinal OCT images and shorten the training time. Firstly, we remove the last several layers from the pre-trained Inception V3 model and regard the remaining part as a fixed feature extractor. Then, the features are used as input of a convolutional neural network (CNN) designed to learn the feature space shifts. The experimental results on two different retinal OCT images datasets ...

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    4. Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning

      Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning

      We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of ...

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    5. 3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement

      3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement

      With the introduction of spectral-domain optical coherence tomography (SDOCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of 3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel ...

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    6. A novel edge tracking approach for cornea in optical coherence tomography anterior chamber images

      A novel edge tracking approach for cornea in optical coherence tomography anterior chamber images

      A new edge tracking method for cornea in optical coherence tomography anterior chamber images has been proposed in the paper. The new approach detects the edge of cornea outside the cornea first. Then, it fixes the detected edge, which follows the future knowledge of cornea, to make sure that only the real edge of cornea could be left. Finally, the method fits the fixed edge by fourth order least squares. The advantage of this proposed method is that it could fit the edge of cornea even for abnormal corneas.

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    7. OCT Segmentation Survey and Summary Review and a Novel 3D Segmentation Algorithm and a Proof of Concept Implementation

      OCT Segmentation Survey and Summary Review and a Novel 3D Segmentation Algorithm and a Proof of Concept Implementation

      We overview the existing OCT work, especially the practical aspects of it. We create a novel algorithm for 3D OCT segmentation with the goals of speed and/or accuracy while remaining exible in the design and implementation for future extensions and improvements. The document at this point is a running draft being iteratively \developed" as a progress report as the work and survey advance. It contains the review and summarization of select OCT works, the design and implementation of the OCTMARF experimentation application and some results.

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    8. A 3D Segmentation Method for Retinal Optical Coherence Tomography Volume Data

      A 3D Segmentation Method for Retinal Optical Coherence Tomography Volume Data

      With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We present a new 3D segmentation method for retinal OCT volume data, which generates an enhanced volume data by using pixel intensity, boundary position information, intensity changes on both sides of the border simultaneously, and preliminary discrete boundary points are found from all A-Scans and then the smoothed boundary surface can be obtained after removing a small ...

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    9. Method for optical coherence tomography image classification using local features and earth mover's distance

      Optical coherence tomography (OCT) is a recent imaging method that allows high-resolution, cross-sectional imaging through tissues and materials. Over the past 18 years, OCT has been successfully used in disease diagnosis, biomedical research, material evaluation, and many other domains. As OCT is a recent imaging method, until now surgeons have limited experience using it. In addition, the number of images obtained from the imaging device is too large, so we need an automated method to analyze them. We propose a novel method for automated classification of OCT images based on local features and earth mover's distance (EMD). We evaluated ...
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    1-9 of 9
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    A 3D Segmentation Method for Retinal Optical Coherence Tomography Volume Data OCT Segmentation Survey and Summary Review and a Novel 3D Segmentation Algorithm and a Proof of Concept Implementation A novel edge tracking approach for cornea in optical coherence tomography anterior chamber images 3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images In vivo evaluation of corneal biomechanical properties by optical coherence elastography at different cross-linking irradiances The Association of Optical Coherence Tomography Results With Neuroimaging Signs and Some Clinical Parameters in Idiopathic Intracranial Hypertension Low cost scalable monolithic common path probe design for the application in endoscopic optical coherence tomography Retinal and Choroidal Vascular Changes in Eyes with Pseudoexfoliation Syndrome: a Comparative Study Using Optical Coherence Tomography Angiography