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

Qiu et al., 2023 - Google Patents

Corsegrec: a topology-preserving scheme for extracting fully-connected coronary arteries from ct angiography

Qiu et al., 2023

View PDF
Document ID
1772580289513068871
Author
Qiu Y
Li Z
Wang Y
Dong P
Wu D
Yang X
Hong Q
Shen D
Publication year
Publication venue
International Conference on Medical Image Computing and Computer-Assisted Intervention

External Links

Snippet

Accurate extraction of coronary arteries from coronary computed tomography angiography (CCTA) is a prerequisite for the computer-aided diagnosis of coronary artery disease (CAD). Deep learning-based methods can achieve automatic segmentation of vasculatures, but few …
Continue reading at www.researchgate.net (PDF) (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
    • G06T2207/30101Blood vessel; Artery; Vein; Vascular
    • 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
    • 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
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • G06T2207/30048Heart; Cardiac
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30172Centreline of tubular or elongated structure
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • 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
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00Indexing scheme for image data processing or generation, in general
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means

Similar Documents

Publication Publication Date Title
Robben et al. Simultaneous segmentation and anatomical labeling of the cerebral vasculature
Rebouças Filho et al. Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images
Chen et al. Curve-like structure extraction using minimal path propagation with backtracking
Xu et al. Noisy labels are treasure: mean-teacher-assisted confident learning for hepatic vessel segmentation
Qiu et al. Corsegrec: a topology-preserving scheme for extracting fully-connected coronary arteries from ct angiography
Pawar et al. LungSeg-Net: Lung field segmentation using generative adversarial network
Zeng et al. Imagecas: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images
JP5661468B2 (en) Computer-aided detection of disease (CAD)
Tan et al. Segmentation of lung airways based on deep learning methods
Zhang et al. Progressive deep segmentation of coronary artery via hierarchical topology learning
Meng et al. Dual consistency enabled weakly and semi-supervised optic disc and cup segmentation with dual adaptive graph convolutional networks
CN113168912A (en) Determining growth rate of objects in 3D data sets using deep learning
Zhang et al. LungSeek: 3D Selective Kernel residual network for pulmonary nodule diagnosis
Liu et al. FTMF-net: A Fourier transform-multiscale feature fusion network for segmentation of small polyp objects
Du et al. Automated coronary artery tree segmentation in coronary CTA using a multiobjective clustering and toroidal model-guided tracking method
Ma et al. A coronary artery segmentation method based on region growing with variable sector search area
Shabani et al. Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning
Wan et al. Automatic vessel segmentation in x-ray angiogram using spatio-temporal fully-convolutional neural network
Wang et al. A 3D tubular flux model for centerline extraction in neuron volumetric images
Guo et al. Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing
Zhang et al. X-ray coronary centerline extraction based on C-UNet and a multifactor reconnection algorithm
Lesage et al. Bayesian maximal paths for coronary artery segmentation from 3D CT angiograms
Thuy et al. Coronary Vessel Segmentation by Coarse‐to‐Fine Strategy Using U‐nets
Ke et al. A scale-aware UNet++ model combined with attentional context supervision and adaptive Tversky loss for accurate airway segmentation
Liu et al. Two new stenosis detection methods of coronary angiograms