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

Korez et al., 2017 - Google Patents

Intervertebral disc segmentation in MR images with 3D convolutional networks

Korez et al., 2017

Document ID
4477909789166923722
Author
Korez R
Ibragimov B
Likar B
Pernuš F
Vrtovec T
Publication year
Publication venue
Medical Imaging 2017: Image Processing

External Links

Snippet

The vertebral column is a complex anatomical construct, composed of vertebrae and intervertebral discs (IVDs) supported by ligaments and muscles. During life, all components undergo degenerative changes, which may in some cases cause severe, chronic and …
Continue reading at www.spiedigitallibrary.org (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
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • 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
    • 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
    • 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
    • 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
    • G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
    • G06K9/527—Scale-space domain transformation, e.g. with wavelet analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS

Similar Documents

Publication Publication Date Title
Korez et al. Model-based segmentation of vertebral bodies from MR images with 3D CNNs
Liu et al. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging
Cheng et al. Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks
Alex et al. Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation
Liu SUSAN: segment unannotated image structure using adversarial network
Cheng et al. Active appearance model and deep learning for more accurate prostate segmentation on MRI
AlZu'bi et al. Transferable hmm trained matrices for accelerating statistical segmentation time
Qadri et al. Vertebrae segmentation via stacked sparse autoencoder from computed tomography images
Bijar et al. Atlas-based automatic generation of subject-specific finite element tongue meshes
Cheng et al. Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections
Xu et al. 3D‐SIFT‐Flow for atlas‐based CT liver image segmentation
Tang et al. Medical image registration: A review
Becker et al. Deformable models in medical image segmentation
Buerger et al. Combining deep learning and model-based segmentation for labeled spine CT segmentation
Poudel et al. Active contours extension and similarity indicators for improved 3D segmentation of thyroid ultrasound images
Zhang et al. A diffeomorphic unsupervised method for deformable soft tissue image registration
Korez et al. Intervertebral disc segmentation in MR images with 3D convolutional networks
Zabihollahy et al. Fully automated segmentation of left ventricular myocardium from 3D late gadolinium enhancement magnetic resonance images using a U-net convolutional neural network-based model
Wu et al. A fully convolutional network feature descriptor: Application to left ventricle motion estimation based on graph matching in short-axis MRI
Iyer et al. Mesh2ssm: From surface meshes to statistical shape models of anatomy
Wan et al. Simultaneous MR knee image segmentation and bias field correction using deep learning and partial convolution
Corral Acero et al. Left ventricle quantification with cardiac MRI: deep learning meets statistical models of deformation
Haq et al. Deformable multisurface segmentation of the spine for orthopedic surgery planning and simulation
Cobzas et al. Random walks for deformable image registration
Brosch et al. Model-based segmentation using neural network-based boundary detectors: Application to prostate and heart segmentation in MR images