1. Articles mentioning both David L. Wilson and Case Western Reserve University

    1-24 of 33 1 2 »
    1. Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

      Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features
      For intravascular OCT (IVOCT) images, we developed an automated atherosclerotic plaque characterization method that used a hybrid learning approach, which combined deep-learning convolutional and hand-crafted, lumen morphological features. Processing was done on innate A-line units with labels fibrolipidic (fibrous tissue followed by lipidous tissue), fibrocalcific (fibrous tissue followed by calcification), or other. We trained/tested on an expansive data set (6,556 images), and performed an active learning, relabeling step ...
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    2. Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence Tomography

      Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence Tomography
      Intravascular optical coherence tomography (IVOCT) is used to assess stent tissue coverage and malapposition in stent evaluation trials. We developed the OCT Image Visualization and Analysis Toolkit for Stent (OCTivat-Stent), for highly automated analysis of IVOCT pullbacks. Algorithms automatically detected the guidewire, lumen boundary, and stent struts; determined the presence of tissue coverage for each strut; and estimated the stent contour for comparison of stent and lumen area. Strut-level tissue ...
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    3. Optical Coherence Tomography-Based Modeling of Stent Deployment in Heavily Calcified Coronary Lesion

      Optical Coherence Tomography-Based Modeling of Stent Deployment in Heavily Calcified Coronary Lesion
      In this work, a heavily calcified coronary artery model was reconstructed from optical coherence tomography (OCT) images to investigate the impact of calcification characteristics on stenting outcomes. The calcification was quantified at various cross sections in terms of angle, maximum thickness, and area. The stent deployment procedure, including the crimping, expansion, and recoil, was implemented. The influence of calcification characteristics on stent expansion, malapposition, and lesion mechanics was characterized. Results ...
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    4. Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images

      Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images
      Accurate identification of coronary plaque is very important for cardiologists when treating patients with advanced atherosclerosis. We developed fully-automated semantic segmentation of plaque in intravascular OCT images. We trained/tested a deep learning model on a folded, large, manually annotated clinical dataset. The sensitivities/specificities were 87.4%/89.5% and 85.1%/94.2% for pixel-wise classification of lipidous and calcified plaque, respectively. Automated clinical lesion metrics, potentially useful ...
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    5. Automated intravascular plaque classification

      Automated intravascular plaque classification
      Methods and apparatus automatically classify intravascular plaque using features extracted from intravascular optical coherence tomography (IVOCT) imagery. One example apparatus includes an image acquisition circuit that accesses a set of IVOCT images, a pre-processing circuit that generates a blood vessel mask based on the IVOCT images, a feature extraction circuit that defines a three dimensional (3D) volume of interest centered on a location in a member of the set of ...
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    6. Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets

      Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets
      We developed machine learning methods to identify fibrolipidic and fibrocalcific A-lines in intravascular optical coherence tomography (IVOCT) images using a comprehensive set of handcrafted features. We incorporated features developed in previous studies (e.g., optical attenuation and A-line peaks). In addition, we included vascular lumen morphology and three-dimensional (3-D) digital edge and texture features. Classification methods were developed using expansive datasets (7000images), consisting of both clinical in-vivo images and an ...
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    7. Postdoc Opening in Coronary Artery, Intravascular OCT Computational Imaging at Case Western Reserve University

      Postdoc Opening in Coronary Artery, Intravascular OCT Computational Imaging at Case Western Reserve University
      ...rans, members of underrepresented minority groups, and individuals with disabilities are encouraged to apply. Case Western Reserve University provides reasonable accommodations to applicants with disabilities. Applicants...
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    8. Case Western Reserve University Receives NIH Grant for Computer Assisted Coronary Artery Stent Interventions

      Case Western Reserve University Receives NIH Grant for Computer Assisted Coronary Artery Stent Interventions
      ...ll build on expertise in interventions, quantitative image analysis of IVOCT, and finite element modeling.

      Case Western Reserve University Receives a 2019 NIH Grant for $732,934 for Computer Assisted Coronary Artery S...

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    9. Automated analysis of intravascular OCT image volumes

      Automated analysis of intravascular OCT image volumes
      This disclosure provides systems and methods to automatically classify stent struts as covered or uncovered and to measure the thickness of tissue coverage. As one example, the method includes storing three-dimensional image data acquired intravascularly via an optical coherence tomography (OCT) apparatus and detecting struts based on analysis of the image data. Image data corresponding to each of the detected struts is further analyzed automatically to compute an indication of ...
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    10. Analysis of optical tomography (OCT) images

      Analysis of optical tomography (OCT) images
      A method includes storing three-dimensional image data acquired intravascularly via an optical coherence tomography (OCT) apparatus. The image data is analyzed to compute a probability estimate of stent presence at support positions appearing in an A-line. Stent strut locations are located in three-dimensional space based on the computed probability estimate of stent presence.
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    11. Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images

      Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images
      We develop neural-network-based methods for classifying plaque types in clinical intravascular optical coherence tomography (IVOCT) images of coronary arteries. A single IVOCT pullback can consist of 500 microscopic-resolution images, creating both a challenge for physician interpretation during an interventional procedure and an opportunity for automated analysis. In the proposed method, we classify each A-line, a datum element that better captures physics and pathophysiology than a voxel, as a fibrous layer ...
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    12. Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography

      Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography
      An estimated 17.5 million people died from a cardiovascular disease in 2012, representing 31 percent of all global deaths. Most acute coronary events result from rupture of the protective fibrous cap overlying an atherosclerotic plaque. The task of early identification of plaque types that can potentially rupture is, therefore, of great importance. The state-of-the-art approach to imaging blood vessels is intravascular optical coherence tomography (IVOCT). However, currently, this is ...
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    13. Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation

      Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation
      Evidence suggests high-resolution, high-contrast, 100 frames / s 100frames/s intravascular optical coherence tomography (IVOCT) can distinguish plaque types, but further validation is needed, especially for automated plaque characterization. We developed experimental and three-dimensional (3-D) registration methods to provide validation of IVOCT pullback volumes using microscopic, color, and fluorescent cryo-image volumes with optional registered cryo-histology. A specialized registration method matched IVOCT pullback images acquired in the catheter reference frame to a ...
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    14. Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks

      Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks
      Analysis of intravascular optical coherence tomography (IVOCT) data has potential for real-time in vivo plaque classification. We developed a processing pipeline on a three-dimensional local region of support for estimation of optical properties of atherosclerotic plaques from coronary artery, IVOCT pullbacks. Using realistic coronary artery disease phantoms, we determined insignificant differences in mean and standard deviation estimates between our pullback analyses and more conventional processing of stationary acquisitions with frame ...
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    15. 3D registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation

      3D registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation
      High resolution, 100 frames/sec intravascular optical coherence tomography (IVOCT) can distinguish plaque types, but further validation is needed, especially for automated plaque characterization. We developed experimental and 3D registration methods, to provide validation of IVOCT pullback volumes using microscopic, brightfield and fluorescent cryoimage volumes, with optional, exactly registered cryo-histology. The innovation was a method to match an IVOCT pullback images, acquired in the catheter reference frame, to a true ...
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    16. Classification of calcium in intravascular OCT images for the purpose of intervention planning

      Classification of calcium in intravascular OCT images for the purpose of intervention planning
      The presence of extensive calcification is a primary concern when planning and implementing a vascular percutaneous intervention such as stenting. If the balloon does not expand, the interventionalist must blindly apply high balloon pressure, use an atherectomy device, or abort the procedure. As part of a project to determine the ability of Intravascular Optical Coherence Tomography (IVOCT) to aid intervention planning, we developed a method for automatic classification of calcium ...
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    17. 3-D Stent Detection in Intravascular OCT Using a Bayesian Network and Graph Search

      3-D Stent Detection in Intravascular OCT Using a Bayesian Network and Graph Search
      Worldwide, many hundreds of thousands of stents are implanted each year to revascularize occlusions in coronary arteries. Intravascular optical coherence tomography (OCT) is an important emerging imaging technique, which has the resolution and contrast necessary to quantitatively analyze stent deployment and tissue coverage following stent implantation. Automation is needed, as current, it takes up to 16 hours to manually analyze hundreds of images and thousands of stent struts from a ...
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    18. Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography

      Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography
      We developed robust, three-dimensional methods, as opposed to traditional A-line analysis, for estimating the optical properties of calcified, fibrotic, and lipid atherosclerotic plaques from in vivo coronary artery intravascular optical coherence tomography clinical pullbacks. We estimated attenuation t and backscattered intensity I 0 from small volumes of interest annotated by experts in 35 pullbacks. Some results were as follows: noise reduction filtering was desirable, parallel line (PL) methods outperformed individual ...
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    19. Differences determined by optical coherence tomography volumetric analysis in non-culprit lesion morphology and inflammation in ST-segment elevation myocardial infarction and stable angina pectoris patients

      Differences determined by optical coherence tomography volumetric analysis in non-culprit lesion morphology and inflammation in ST-segment elevation myocardial infarction and stable angina pectoris patients
      .... Zhao Wang PhD^2, 3. Hiram G. Bezerra MD, PhD^3, 4. Pedro Alves Lemos MD, PhD^1, 5. Audrey Schnell PhD^3, 6. David L. Wilson PhD^2, 7. Andrew M. Rollins PhD^2, 8. Marco A. Costa MD, PhD^3,† and 9. Guilherme F....
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    20. Segmentation and quantification for intravascular optical coherence tomography images

      Segmentation and quantification for intravascular optical coherence tomography images
      A system and related methods for automatic or semi-automatic segmentation and quantification of blood vessel structure and physiology, including segmentation and quantification of lumen, guide wire, vessel wall, calcified plaques, fibrous caps, macrophages, metallic and bioresorbable stents are described, and including visualization of results. Calcified plaque segmentation can be used to estimate the distribution of superficial calcification and inform strategies stenting. Volumetric segmentation and quantification of fibrous caps can provide ...
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    21. Feature Of The Week 1/20/13: CWRU Demonstrates Automatic Stent Detection in Intravascular OCT Images Using Bagged Decision Trees

      Feature Of The Week 1/20/13: CWRU Demonstrates Automatic Stent Detection in Intravascular OCT Images Using Bagged Decision Trees
      ...ntly reduce from the 6-16 hours now required. For more information see recent Article . Courtesy Hong Lu from Case Western Reserve University . Cardiovascular disease is the leading cause of death worldwide. Stent implan...
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    22. Automatic stent detection in intravascular OCT images using bagged decision trees

      Automatic stent detection in intravascular OCT images using bagged decision trees
      Intravascular optical coherence tomography (iOCT) is being used to assess viability of new coronary artery stent designs. We developed a highly automated method for detecting stent struts and measuring tissue coverage. We trained a bagged decision trees classifier to classify candidate struts using features extracted from the images. With 12 best features identified by forward selection, recall (precision) were 90%94% (85%90%). Including struts deemed insufficiently bright for manual ...
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    23. Volumetric quantification of fibrous caps using intravascular optical coherence tomography

      Volumetric quantification of fibrous caps using intravascular optical coherence tomography
      The rupture of thin-cap fibroatheroma accounts for most acute coronary events. Optical Coherence Tomography (OCT) allows quantification of fibrous cap (FC) thickness in vivo. Conventional manual analysis, by visually determining the thinnest part of the FC is subject to inter-observer variability and does not capture the 3-D morphology of the FC. We propose and validate a computer-aided method that allows volumetric analysis of FC. The radial FC boundary is semi-automatically ...
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    24. 1-24 of 33 1 2 »
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  3. People in the News

    1. (22 articles) Hiram G. Bezerra
    2. (18 articles) Andrew M. Rollins
    3. (12 articles) Marco A. Costa
    4. (12 articles) Madhusudhana Gargesha
    5. (10 articles) Zhao Wang
    6. (7 articles) Michael W. Jenkins
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