1. Articles from Soumya Ray

    1-6 of 6
    1. 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 IVOCT images, a classification circuit that generates a classification based on a probability that a voxel represents a type of plaque, and a visualization circuit that provides a visualization, substantially ...

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    2. 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 an offline approach where the images are first collected and then manually analyzed an image at a time to identify regions at risk of thrombosis. This process is extremely laborious ...

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    3. 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 averaging. There was no effect of tissue depth or oblique imaging on pullback parameter estimates. The method’s performance was assessed in comparison with observer-defined standards using clinical pullback data ...

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    4. 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 in coronary IVOCT images. We developed an approach where plaque texture is modeled by the joint probability distribution of a bank of filter responses where the filter bank was chosen ...

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    5. 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

      Cardiovascular disease is the leading cause of death worldwide. Stent implantation by means of percutaneous coronary intervention is the most common coronary revascularization procedure. Intravascular Optical Coherence Tomography (iOCT) is the only imaging modality with the resolution and contrast necessary to enable accurate measurements of luminal architecture and neointima stent coverage. Manual analysis of intravascular OCT pullbacks is time consuming, limiting the size and number of studies that can be performed. We developed a highly automated method for detecting stent struts and measuring tissue coverage. Candidate struts were first identified using image processing techniques. We trained a bagged decision trees ...

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    6. 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 analysis, precision improved to 94%. Strut detection statistics approached variability of manual analysis. Differences between manual and automatic area measurements were 0.12 ± 0.20 mm 2 and 0.11 ...

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    1-6 of 6
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  2. Topics in the News

    1. (6 articles) Case Western Reserve University
    2. (6 articles) David L. Wilson
    3. (5 articles) Hiram G. Bezerra
    4. (4 articles) Andrew M. Rollins
    5. (3 articles) Madhusudhana Gargesha
    6. (2 articles) Marco A. Costa
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    12. (1 articles) University of Auckland
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    14. (1 articles) Cleveland Clinic
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    16. (1 articles) Frederique Vanholsbeeck
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    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 Classification of calcium in intravascular OCT images for the purpose of intervention planning Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography Automated intravascular plaque classification Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification Functional characteristics of glaucoma related arcuate defects seen on OCT en face visualisation of the retinal nerve fibre layer Heads-up Digitally Assisted Surgical Viewing with Intraoperative Optical Coherence Tomography for Myopic Schisis Repair Optical Coherence Tomography Findings in Nodular Anterior Scleritis due to Post-Streptococcal Syndrome Comparison of Choroidal Thickness Measurements between Spectral-domain Optical Coherence Tomography and Swept-source Optical Coherence Tomography in Children Recent advances and future directions on the use of optical coherence tomography in neuro-ophthalmology