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

Chen et al., 2023 - Google Patents

Radiologically based automated segmentation of cardiac MRI using an improved U-Net neural algorithm

Chen et al., 2023

View HTML
Document ID
15654550566266394823
Author
Chen Y
Wang L
Ding B
Huang Y
Wen T
Huang J
Publication year
Publication venue
Journal of Radiation Research and Applied Sciences

External Links

Snippet

Objective To overcome the complexity and variability of the heart, along with factors like fuzzy boundaries and low contrast and produce automated segmentation based on machine learning. Methods Segmentation of cardiac MRI plays a vital role in various clinical …
Continue reading at www.sciencedirect.com (HTML) (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
    • 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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
    • G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • 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
    • 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
    • G06T2207/20101—Interactive definition of point of interest, landmark or seed
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • 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
Haque et al. Deep learning approaches to biomedical image segmentation
Tan et al. Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences
Moradi et al. MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography
Survarachakan et al. Deep learning for image-based liver analysis—A comprehensive review focusing on malignant lesions
Gu et al. Segmentation of coronary arteries images using global feature embedded network with active contour loss
Li et al. EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography
Wu et al. Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images
Ammari et al. A review of approaches investigated for right ventricular segmentation using short‐axis cardiac MRI
Sirjani et al. Automatic cardiac evaluations using a deep video object segmentation network
Zhu et al. A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
Jafari et al. LMISA: A lightweight multi-modality image segmentation network via domain adaptation using gradient magnitude and shape constraint
Singh et al. Attention-guided residual W-Net for supervised cardiac magnetic resonance imaging segmentation
Sharma et al. A novel solution of using deep learning for left ventricle detection: enhanced feature extraction
Alex et al. YSegNet: a novel deep learning network for kidney segmentation in 2D ultrasound images
Singh et al. MADRU-Net: Multiscale attention-based cardiac MRI segmentation using deep residual U-Net
Chen et al. Radiologically based automated segmentation of cardiac MRI using an improved U-Net neural algorithm
Ramadan et al. Medical image registration in the era of Transformers: a recent review
Ragnarsdottir et al. Deep learning based prediction of pulmonary hypertension in newborns using echocardiograms
Zhang et al. Multiple attention fully convolutional network for automated ventricle segmentation in cardiac magnetic resonance imaging
Belfilali et al. Left ventricle analysis in echocardiographic images using transfer learning
Abdelrauof et al. Light-weight localization and scale-independent multi-gate UNET segmentation of left and right ventricles in MRI images
Aghapanah et al. MECardNet: A novel multi-scale convolutional ensemble model with adaptive deep supervision for precise cardiac MRI segmentation
Moglia et al. Deep Learning for Pancreas Segmentation: a Systematic Review
Zheng Deep learning for robust segmentation and explainable analysis of 3d and dynamic cardiac images
Xu et al. A two-stage fully automatic segmentation scheme using both 2D and 3D U-Net for multi-sequence cardiac MR