[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 PDF
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 pmc.ncbi.nlm.nih.gov (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
    • 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
    • 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
    • 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
    • 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
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Hesamian et al. Deep learning techniques for medical image segmentation: achievements and challenges
Wang et al. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks
Tian et al. PSNet: prostate segmentation on MRI based on a convolutional neural network
Larsson et al. Robust abdominal organ segmentation using regional convolutional neural networks
Havaei et al. Deep learning trends for focal brain pathology segmentation in MRI
Hasan et al. CondenseUNet: A memory-efficient condensely-connected architecture for bi-ventricular blood pool and myocardium segmentation
Punn et al. Multi-modality encoded fusion with 3D inception U-net and decoder model for brain tumor segmentation
Zhou et al. Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation
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
Corral Acero et al. A 2-step deep learning method with domain adaptation for multi-centre, multi-vendor and multi-disease cardiac magnetic resonance segmentation
Kausar et al. 3D shallow deep neural network for fast and precise segmentation of left atrium
Sabarinathan et al. Hyper vision net: kidney tumor segmentation using coordinate convolutional layer and attention unit
Wu et al. Image synthesis in contrast MRI based on super resolution reconstruction with multi-refinement cycle-consistent generative adversarial networks
Guo et al. Cardiac MRI left ventricle segmentation and quantification: A framework combining U-Net and continuous max-flow
Rezaei et al. Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis
Zhu et al. New loss functions for medical image registration based on Voxelmorph
Chen et al. CTUNet: automatic pancreas segmentation using a channel-wise transformer and 3D U-Net
Murmu et al. A novel Gateaux derivatives with efficient DCNN-Resunet method for segmenting multi-class brain tumor
Mu et al. Automatic MR kidney segmentation for autosomal dominant polycystic kidney disease
Han et al. Deformable MR-CT image registration using an unsupervised synthesis and registration network for neuro-endoscopic surgery
Prasad et al. Modifying U-Net for small dataset: a simplified U-Net version for liver parenchyma segmentation
Li et al. NVTrans‐UNet: Neighborhood vision transformer based U‐Net for multi‐modal cardiac MR image segmentation
Li et al. Cardiac MRI segmentation with focal loss constrained deep residual networks
Hasan et al. L-CO-Net: Learned condensation-optimization network for segmentation and clinical parameter estimation from cardiac cine MRI