Nothing Special   »   [go: up one dir, main page]

Kim, 2020 - Google Patents

Validation of Functional Analysis in the Left Ventricular Myocardium Using Convolutional Neural Networks (CNN)

Kim, 2020

View PDF
Document ID
9082076583989911487
Author
Kim C
Publication year

External Links

Snippet

Cardiovascular Diseases (CVD) are fatal heart diseases that can cause death regardless of gender, age, race and ethnicity. The statistics shows that one person dies every 37 seconds in the United States from CVD. The heart attack, also known as myocardial infarction, is …
Continue reading at escholarship.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20101—Interactive definition of point of interest, landmark or seed
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
    • G06T3/0037—Reshaping or unfolding a 3D tree structure onto a 2D plane
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications

Similar Documents

Publication Publication Date Title
US8107703B2 (en) Quantitative real-time 4D stress test analysis
Militello et al. A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans
JP6877868B2 (en) Image processing equipment, image processing method and image processing program
van Geuns et al. Automatic quantitative left ventricular analysis of cine MR images by using three-dimensional information for contour detection
Xiong et al. Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest
US7822246B2 (en) Method, a system and a computer program for integration of medical diagnostic information and a geometric model of a movable body
JP2007202957A (en) Wall motion measuring device and medical image diagnostic apparatus
US8755575B2 (en) Transmural perfusion gradient image analysis
Corsi et al. Quantification of regional left ventricular wall motion from real-time 3-dimensional echocardiography in patients with poor acoustic windows: effects of contrast enhancement tested against cardiac magnetic resonance
Lee et al. Machine learning and coronary artery calcium scoring
WO2010020933A2 (en) Processing cardiac data for personalized aha diagram
Jin et al. Left atrial appendage segmentation and quantitative assisted diagnosis of atrial fibrillation based on fusion of temporal-spatial information
Sakellarios et al. Novel methodology for 3D reconstruction of carotid arteries and plaque characterization based upon magnetic resonance imaging carotid angiography data
El Berbari et al. An automated myocardial segmentation in cardiac MRI
Badano et al. Artificial intelligence and cardiovascular imaging: A win-win combination.
Berggren et al. Multiple convolutional neural networks for robust myocardial segmentation
Stalidis et al. Model-based processing scheme for quantitative 4-D cardiac MRI analysis
Olveres et al. Left ventricle Hermite-based segmentation
Tobon-Gomez et al. A multimodal database for the 1 st cardiac motion analysis challenge
Böttger et al. Application of a new segmentation tool based on interactive simplex meshes to cardiac images and pulmonary MRI data
Kim Validation of Functional Analysis in the Left Ventricular Myocardium Using Convolutional Neural Networks (CNN)
Bhan et al. Patient-specific cardiac computational modeling based on left ventricle segmentation from magnetic resonance images
Inoue et al. Patient-specific left atrial wall-thickness measurement and visualization for radiofrequency ablation
Dziri et al. New hybrid method for left ventricular ejection fraction assessment from radionuclide ventriculography images
Tautz et al. Exploration of Interventricular Septum Motion in Multi-Cycle Cardiac MRI.