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

Leclerc et al., 2020 - Google Patents

LU-Net: a multistage attention network to improve the robustness of segmentation of left ventricular structures in 2-D echocardiography

Leclerc et al., 2020

View PDF
Document ID
1037876678701589907
Author
Leclerc S
Smistad E
Østvik A
Cervenansky F
Espinosa F
Espeland T
Berg E
Belhamissi M
Israilov S
Grenier T
Lartizien C
Jodoin P
Lovstakken L
Bernard O
Publication year
Publication venue
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

External Links

Snippet

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semiautomatically in clinical routine and is, thus, prone to interobserver and intraobserver variabilities. Recent studies have shown that …
Continue reading at hal.science (PDF) (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
    • G06T2207/10024—Color 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
    • G06T2207/20112—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
    • G06T2207/10072—Tomographic images
    • 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
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • 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
    • 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
    • G06K2209/05—Recognition of patterns in medical or anatomical images
    • G06K2209/051—Recognition of patterns in medical or anatomical images of internal organs
    • 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
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268—Feature extraction; Face representation
    • G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Leclerc et al. LU-Net: a multistage attention network to improve the robustness of segmentation of left ventricular structures in 2-D echocardiography
Zotti et al. Convolutional neural network with shape prior applied to cardiac MRI segmentation
Leclerc et al. Deep learning for segmentation using an open large-scale dataset in 2D echocardiography
CN110807829B (en) Method for constructing three-dimensional heart model based on ultrasonic imaging
Ali et al. Echocardiographic image segmentation using deep Res-U network
Habijan et al. Overview of the whole heart and heart chamber segmentation methods
Bohlender et al. A survey on shape-constraint deep learning for medical image segmentation
Kitamura et al. Automatic coronary extraction by supervised detection and shape matching
Kim et al. Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks
Yan et al. Cine MRI analysis by deep learning of optical flow: Adding the temporal dimension
Du et al. An integrated deep learning framework for joint segmentation of blood pool and myocardium
Awasthi et al. LVNet: Lightweight model for left ventricle segmentation for short axis views in echocardiographic imaging
Sfakianakis et al. GUDU: Geometrically-constrained Ultrasound Data augmentation in U-Net for echocardiography semantic segmentation
Vepa et al. Weakly-supervised convolutional neural networks for vessel segmentation in cerebral angiography
Merkow et al. Structural edge detection for cardiovascular modeling
Huang et al. POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation
Jiang et al. A dual-stream centerline-guided network for segmentation of the common and internal carotid arteries from 3D ultrasound images
Sharma et al. A novel solution of using deep learning for left ventricle detection: enhanced feature extraction
Brahim et al. A 3D network based shape prior for automatic myocardial disease segmentation in delayed-enhancement MRI
Laumer et al. Weakly supervised inference of personalized heart meshes based on echocardiography videos
Zhang et al. Multiple attention fully convolutional network for automated ventricle segmentation in cardiac magnetic resonance imaging
Deng et al. Active cardiac model and its application on structure detection from early fetal ultrasound sequences
Gungor et al. View classification and object detection in cardiac ultrasound to localize valves via deep learning
Lee et al. True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching
Leclerc et al. Lu-net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2d echocardiography