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

Ryu et al., 2022 - Google Patents

Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the …

Ryu et al., 2022

Document ID
4829845084973782467
Author
Ryu S
Shin K
Shin S
Lee S
Seo S
Cheon S
Ryu S
Kim J
Ji S
Kim N
Publication year
Publication venue
Computers in Biology and Medicine

External Links

Snippet

Landmark detection in flatfoot radiographs is crucial in analyzing foot deformity. Here, we evaluated the accuracy and efficiency of the automated identification of flatfoot landmarks using a newly developed cascade convolutional neural network (CNN) algorithm, Flatfoot …
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
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • 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
    • G06F19/34—Computer-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
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • 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
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • 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
    • G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
    • G06T3/0037—Reshaping or unfolding a 3D tree structure onto a 2D plane
    • 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
    • G06T11/00—2D [Two Dimensional] image generation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/50—Clinical applications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/05—Recognition of patterns in medical or anatomical images

Similar Documents

Publication Publication Date Title
US11284849B2 (en) Dual energy x-ray coronary calcium grading
US10588589B2 (en) Systems and methods for prediction of osteoporotic fracture risk
Dot et al. Accuracy and reliability of automatic three-dimensional cephalometric landmarking
Paniagua et al. Clinical application of SPHARM-PDM to quantify temporomandibular joint osteoarthritis
AU2018376561A1 (en) Three-dimensional medical image analysis method and system for identification of vertebral fractures
JP2018011958A (en) Medical image processing apparatus and medical image processing program
JP2019195627A (en) System and device for analyzing anatomical image
US9211103B2 (en) Systems and methods for generating an osteoarthritis progression predictor and systems and methods for using the predictor
WO2016013004A1 (en) Systems and methods for prediction of osteoporotic fracture risk
Ryu et al. Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot
JP2010515557A (en) Image processing system and method.
Melinska et al. Statistical, morphometric, anatomical shape model (atlas) of calcaneus
Suri et al. Vertebral deformity measurements at MRI, CT, and radiography using deep learning
Meng et al. An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation
Seo et al. Deep focus approach for accurate bone age estimation from lateral cephalogram
Ryu et al. Enhancement of evaluating flatfoot on a weight-bearing lateral radiograph of the foot with U-Net based semantic segmentation on the long axis of tarsal and metatarsal bones in an active learning manner
Guo et al. Automatic analysis system of calcaneus radiograph: rotation-invariant landmark detection for calcaneal angle measurement, fracture identification and fracture region segmentation
Ryu et al. Diagnosis of osteoporotic vertebral compression fractures and fracture level detection using multitask learning with U-Net in lumbar spine lateral radiographs
Arbabi et al. Statistical shape model of the talus bone morphology: A comparison between impinged and nonimpinged ankles
Kao et al. Fully automated determination of arch angle on weight-bearing foot radiograph
Duong et al. Automatic detection of scoliotic curves in posteroanterior radiographs
Makhdoomi et al. Development of Scoliotic Spine Severity Detection using Deep Learning Algorithms
Yuh et al. Deep learning-assisted quantitative measurement of thoracolumbar fracture features on lateral radiographs
Lee et al. Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts
Wang et al. A machine learning method for precise detection of spine bone mineral density