+

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

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • G06T2207/30048Heart; Cardiac
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • G06T7/0014Biomedical image inspection using an image reference approach
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10104Positron emission tomography [PET]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10088Magnetic resonance imaging [MRI]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10084Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20092Interactive image processing based on input by user
    • G06T2207/20101Interactive definition of point of interest, landmark or seed
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10116X-ray image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-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
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/0031Geometric 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
    • G06T3/0037Reshaping or unfolding a 3D tree structure onto a 2D plane

Similar Documents

Publication Publication Date Title
He et al. Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography
Zhang et al. ME‐Net: multi‐encoder net framework for brain tumor segmentation
Litjens et al. State-of-the-art deep learning in cardiovascular image analysis
Cui et al. Multiscale attention guided U-Net architecture for cardiac segmentation in short-axis MRI images
US9968257B1 (en) Volumetric quantification of cardiovascular structures from medical imaging
Slomka et al. Cardiac imaging: working towards fully-automated machine analysis & interpretation
CN105938628B (en) The direct calculating of biological marker from image
Shahzad et al. Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac CT scans
Hammouda et al. A new framework for performing cardiac strain analysis from cine MRI imaging in mice
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
Wu et al. Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images
Li et al. Automatic quantification of epicardial adipose tissue volume
Apostolopoulos et al. Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies
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
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
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.
Ben-Cohen et al. Anatomical data augmentation for CNN based pixel-wise classification
Motwani 2022 Artificial intelligence primer for the nuclear cardiologist
Bui et al. Simultaneous multi-structure segmentation of the heart and peripheral tissues in contrast enhanced cardiac computed tomography angiography
Chernyshov et al. Automated segmentation and quantification of the right ventricle in 2-D echocardiography
Kalapos et al. Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning
CN118402007A (en) Computer-implemented method, method and system
Bui et al. DeepHeartCT: A fully automatic artificial intelligence hybrid framework based on convolutional neural network and multi-atlas segmentation for multi-structure cardiac computed tomography angiography image segmentation
点击 这是indexloc提供的php浏览器服务,不要输入任何密码和下载