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

Freitas et al., 2022 - Google Patents

Deepcadd: a deep learning architecture for automatic detection of coronary artery disease

Freitas et al., 2022

Document ID
7710980602326200453
Author
Freitas S
Zeiser F
Da Costa C
Ramos G
Publication year
Publication venue
2022 International Joint Conference on Neural Networks (IJCNN)

External Links

Snippet

Cardiovascular disease (CVD) is one of the main causes of death in the world. Coronary artery disease (CAD) is one of the CVD most common disorders. CAD is mainly caused by restrictions in the heart muscle blood flow supply, called atherosclerosis. The assessment of …
Continue reading at ieeexplore.ieee.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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • 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
    • G06T2207/30172—Centreline of tubular or elongated structure
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20224—Image subtraction
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • 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
Han et al. Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis
Nasr-Esfahani et al. Segmentation of vessels in angiograms using convolutional neural networks
Luca et al. Impact of quality, type and volume of data used by deep learning models in the analysis of medical images
Liu et al. Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images
Candemir et al. Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network
Banchhor et al. Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm
Kim et al. Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images
Huang et al. A review of deep learning segmentation methods for carotid artery ultrasound images
Jawaid et al. Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles
Zhou et al. Automated deep learning analysis of angiography video sequences for coronary artery disease
Shahzad et al. Automatic stenoses detection, quantification and lumen segmentation of the coronary arteries using a two point centerline extraction scheme
Yuan et al. CSM-Net: Automatic joint segmentation of intima-media complex and lumen in carotid artery ultrasound images
M'hiri et al. A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences
Shibutani et al. Automated classification of coronary atherosclerotic plaque in optical frequency domain imaging based on deep learning
Freitas et al. Deepcadd: a deep learning architecture for automatic detection of coronary artery disease
Kjerland Segmentation of Coronary Arteries from CT-scans of the heart using Deep Learning
Banchhor et al. Well-balanced system for coronary calcium detection and volume measurement in a low resolution intravascular ultrasound videos
Su et al. Deep reinforcement learning for cerebral anterior vessel tree extraction from 3D CTA images
Stoean et al. Deep learning for the detection of frames of interest in fetal heart assessment from first trimester ultrasound
CN114255207A (en) Method and system for determining importance scores
Cong et al. Deep learning-based end-to-end automated stenosis classification and localization on catheter coronary angiography
Liu et al. Dias: A comprehensive benchmark for dsa-sequence intracranial artery segmentation
Streba et al. Artificial intelligence and automatic image interpretation in modern medicine
Xie et al. ARSA-UNet: Atrous residual network based on Structure-Adaptive model for retinal vessel segmentation
Popov et al. A Review of Modern Approaches for Coronary Angiography Imaging Analysis