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

Zhao et al., 2023 - Google Patents

Glenoid segmentation from computed tomography scans based on a 2-stage deep learning model for glenoid bone loss evaluation

Zhao et al., 2023

Document ID
4982508512592491556
Author
Zhao Q
Feng Q
Zhang J
Xu J
Wu Z
Huang C
Yuan H
Publication year
Publication venue
Journal of Shoulder and Elbow Surgery

External Links

Snippet

Background The best-fitting circle drawn by computed tomography (CT) reconstruction of the en face view of the glenoid bone to measure the bone defect is widely used in clinical application. However, there are still some limitations in practical application, which can …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • G06T7/0014Biomedical image inspection using an image reference approach
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10088Magnetic resonance imaging [MRI]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/3437Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/05Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/053Measuring electrical impedance or conductance of a portion of the body
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/05Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/055Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves involving electronic or nuclear magnetic resonance, e.g. magnetic resonance imaging
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10Services
    • G06Q50/22Health care, e.g. hospitals; Social work

Similar Documents

Publication Publication Date Title
Yeoh et al. Emergence of deep learning in knee osteoarthritis diagnosis
Lansdown et al. Automated 3-dimensional magnetic resonance imaging allows for accurate evaluation of glenoid bone loss compared with 3-dimensional computed tomography
Zhang et al. Deep learning approach for anterior cruciate ligament lesion detection: evaluation of diagnostic performance using arthroscopy as the reference standard
US20130137962A1 (en) Method for Detecting Arthritis and Cartilage Damage Using Magnetic Resonance Sequences
Ahedi et al. Hip shape as a predictor of osteoarthritis progression in a prospective population cohort
Rozynek et al. Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives
McGuffin et al. Is the contralateral hip at risk in patients with unilateral symptomatic cam femoroacetabular impingement? A quantitative T1ρ MRI study
CN116705300A (en) Medical decision assistance method, system and storage medium based on sign data analysis
Cigdem et al. Artificial intelligence in knee osteoarthritis: a comprehensive review
Davis et al. Correlation of quantitative versus semiquantitative measures of supraspinatus intramuscular fatty infiltration to shoulder range of motion and strength: a pilot study
US20170202520A1 (en) Method for predicting the development of arthritis in individuals prior to radiographic or symptomatic presentation
Zhao et al. Glenoid segmentation from computed tomography scans based on a 2-stage deep learning model for glenoid bone loss evaluation
Felfeliyan et al. MRI knee domain translation for unsupervised segmentation by CycleGAN (data from osteoarthritis initiative (OAI))
Tibrewala et al. Automatic hip abductor muscle fat fraction estimation and association with early OA cartilage degeneration biomarkers
Thomas et al. Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning
Pedoia et al. Translation of morphological and functional musculoskeletal imaging
Luo et al. Prediction of negative conversion days of childhood nephrotic syndrome based on PCA and BP-AdaBoost neural network
US20160213278A1 (en) Method for Detecting Arthritis and Cartilage Damage Using Magnetic Resonance Sequences
Grzywińska et al. Computation of the texture features on T2-weighted images as a novel method to assess the function of the transplanted kidney: primary research
Wilson et al. Integrating MR imaging with full-surface indentation mapping of femoral cartilage in an ex vivo porcine stifle
Bugeja et al. Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images
Postma et al. The potential for clinical application of automatic quantification of olfactory bulb volume in MRI scans using convolutional neural networks
Patil et al. Classification and risk estimation of osteoarthritis using deep learning methods
Nyee et al. The Design and Development of Automated Knee Cartilage Segmentation Framework
US20220398763A1 (en) Systems and methods of volumetrically assessing structures of skeletal cavities