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

Ammari et al., 2023 - Google Patents

Deep-active-learning approach towards accurate right ventricular segmentation using a two-level uncertainty estimation

Ammari et al., 2023

Document ID
16484597997656025448
Author
Ammari A
Mahmoudi R
Hmida B
Saouli R
Bedoui M
Publication year
Publication venue
Computerized Medical Imaging and Graphics

External Links

Snippet

Abstract The Right Ventricle (RV) is currently recognised to be a significant and important prognostic factor for various pathologies. Its assessment is made possible using Magnetic Resonance Imaging (CMRI) short-axis slices. Yet, due to the challenging issues of this …
Continue reading at www.sciencedirect.com (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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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
    • G06T2207/10104—Positron emission tomography [PET]
    • 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
    • G06T2207/20156—Automatic seed setting
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user

Similar Documents

Publication Publication Date Title
Tong et al. 3D deeply-supervised U-net based whole heart segmentation
US11101033B2 (en) Medical image aided diagnosis method and system combining image recognition and report editing
Khened et al. Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest
Wang et al. LT-Net: Label transfer by learning reversible voxel-wise correspondence for one-shot medical image segmentation
Ammar et al. Automatic cardiac cine MRI segmentation and heart disease classification
Poudel et al. Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation
Liu et al. Automated cardiac segmentation of cross-modal medical images using unsupervised multi-domain adaptation and spatial neural attention structure
Xiao et al. Efficient combination of CNN and transformer for dual-teacher uncertainty-guided semi-supervised medical image segmentation
Altini et al. Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey
Zhao et al. Attention-based generative adversarial network in medical imaging: A narrative review
Cui et al. Bidirectional cross-modality unsupervised domain adaptation using generative adversarial networks for cardiac image segmentation
CN110363772B (en) Cardiac MRI segmentation method and system based on adversarial network
Xue et al. Deep hybrid neural-like P systems for multiorgan segmentation in head and neck CT/MR images
Xia et al. Automatic 3D+ t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale
Wang et al. SK-Unet: An improved U-net model with selective kernel for the segmentation of multi-sequence cardiac MR
Yu et al. Multi-level multi-type self-generated knowledge fusion for cardiac ultrasound segmentation
Sfakianakis et al. GUDU: Geometrically-constrained Ultrasound Data augmentation in U-Net for echocardiography semantic segmentation
Qayyum et al. Unsupervised unpaired multiple fusion adaptation aided with self-attention generative adversarial network for scar tissues segmentation framework
Lefebvre et al. LASSNet: a four steps deep neural network for left atrial segmentation and scar quantification
Tejero-de-Pablos et al. Texture-based classification of significant stenosis in CCTA multi-view images of coronary arteries
Ammari et al. Deep-active-learning approach towards accurate right ventricular segmentation using a two-level uncertainty estimation
Liu et al. Toward automated right ventricle segmentation via edge feature-induced self-attention multiscale feature aggregation full convolution network
CN116309385A (en) Method and system for measuring abdominal fat and muscle tissue based on weakly supervised learning
Yong et al. Automatic ventricular nuclear magnetic resonance image processing with deep learning
Su et al. Res-DUnet: A small-region attentioned model for cardiac MRI-based right ventricular segmentation