[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

He et al., 2020 - Google Patents

Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography

He et al., 2020

Document ID
9478839834883569699
Author
He X
Guo B
Lei Y
Wang T
Fu Y
Curran W
Zhang L
Liu T
Yang X
Publication year
Publication venue
Physics in Medicine & Biology

External Links

Snippet

Epicardial adipose tissue (EAT) is a visceral fat deposit, that's known for its association with factors, such as obesity, diabetes mellitus, age, and hypertension. Segmentation of the EAT in a fast and reproducible way is important for the interpretation of its role as an independent …
Continue reading at iopscience.iop.org (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
    • G06T2207/10104—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
    • G06T2207/10—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
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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
    • 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
    • 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
    • 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
    • G06T2207/10116—X-ray image
    • 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
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • 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
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • 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
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • 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

Similar Documents

Publication Publication Date Title
Zhang et al. ME‐Net: multi‐encoder net framework for brain tumor segmentation
He et al. Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography
Su et al. Lung nodule detection based on faster R-CNN framework
Leiner et al. Machine learning in cardiovascular magnetic resonance: basic concepts and applications
Militello et al. A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans
Slomka et al. Cardiac imaging: working towards fully-automated machine analysis & interpretation
US9968257B1 (en) Volumetric quantification of cardiovascular structures from medical imaging
Cui et al. Multiscale attention guided U-Net architecture for cardiac segmentation in short-axis MRI images
Penso et al. Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network
Shahzad et al. Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac CT scans
Vesal et al. Fully automated 3d cardiac mri localisation and segmentation using deep neural networks
Lee et al. Machine learning and coronary artery calcium scoring
Slomka et al. Application and translation of artificial intelligence to cardiovascular imaging in nuclear medicine and noncontrast CT
Cai et al. A framework combining window width-level adjustment and Gaussian filter-based multi-resolution for automatic whole heart segmentation
Li et al. Automatic quantification of epicardial adipose tissue volume
van Velzen et al. AI-based quantification of planned radiation therapy dose to cardiac structures and coronary arteries in patients with breast cancer
Lin et al. Cascaded triplanar autoencoder M-Net for fully automatic segmentation of left ventricle myocardial scar from three-dimensional late gadolinium-enhanced MR images
Wang et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging
Priya et al. Adaptive fruitfly based modified region growing algorithm for cardiac fat segmentation using optimal neural network
Badano et al. Artificial intelligence and cardiovascular imaging: A win-win combination.
Wong et al. Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI
Kalapos et al. Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning
Bourfiss et al. Towards automatic classification of cardiovascular magnetic resonance Task Force Criteria for diagnosis of arrhythmogenic right ventricular cardiomyopathy
Kulasekara et al. Comparison of two-dimensional and three-dimensional U-Net architectures for segmentation of adipose tissue in cardiac magnetic resonance images
Chernyshov et al. Automated Segmentation and Quantification of the Right Ventricle in 2-D Echocardiography