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

Hasan et al., 2020 - Google Patents

CondenseUNet: A memory-efficient condensely-connected architecture for bi-ventricular blood pool and myocardium segmentation

Hasan et al., 2020

View HTML
Document ID
17398782278209847615
Author
Hasan S
Linte C
Publication year
Publication venue
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling

External Links

Snippet

With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there has been a paradigm shift in medical technology, thanks to its capability of imaging different structures within the heart without ionizing radiation. However, it is very challenging to conduct pre …
Continue reading at www.ncbi.nlm.nih.gov (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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
    • 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
    • G06T7/00—Image analysis
    • 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
    • G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/0068—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping
    • G06T3/0081—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping by elastic snapping
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Hesamian et al. Deep learning techniques for medical image segmentation: achievements and challenges
Bernal et al. Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
Hasan et al. CondenseUNet: A memory-efficient condensely-connected architecture for bi-ventricular blood pool and myocardium segmentation
Tang et al. CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation
Sun et al. Deformable MRI-ultrasound registration using 3D convolutional neural network
Punn et al. Multi-modality encoded fusion with 3D inception U-net and decoder model for brain tumor segmentation
Kaur et al. GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets
Liu et al. Image synthesis-based multi-modal image registration framework by using deep fully convolutional networks
Ahmad et al. Convolutional-neural-network-based feature extraction for liver segmentation from CT images
Wang et al. Automatic MRI prostate segmentation using 3D deeply supervised FCN with concatenated atrous convolution
Carton et al. Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net
Heinrich et al. Voxelmorph++ going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation
Kausar et al. 3D shallow deep neural network for fast and precise segmentation of left atrium
Wu et al. Image synthesis in contrast MRI based on super resolution reconstruction with multi-refinement cycle-consistent generative adversarial networks
Murali et al. Generative modeling for renal microanatomy
Guo et al. Cardiac MRI left ventricle segmentation and quantification: A framework combining U-Net and continuous max-flow
Zhong et al. Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images
Han et al. Deformable MR-CT image registration using an unsupervised synthesis and registration network for neuro-endoscopic surgery
Dorgham et al. U-NetCTS: U-Net deep neural network for fully automatic segmentation of 3D CT DICOM volume
Prasad et al. Modifying U-Net for small dataset: a simplified U-Net version for liver parenchyma segmentation
Murmu et al. A novel Gateaux derivatives with efficient DCNN-Resunet method for segmenting multi-class brain tumor
Li et al. Cardiac MRI segmentation with focal loss constrained deep residual networks
Zeng et al. Learning-based US-MR liver image registration with spatial priors
Hasan et al. L-CO-Net: Learned condensation-optimization network for segmentation and clinical parameter estimation from cardiac cine MRI
Corral Acero et al. Left ventricle quantification with cardiac MRI: deep learning meets statistical models of deformation