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

Guan et al., 2024 - Google Patents

Diagnostic value of rEBUS-TBLB combined distance measurement method based on ultrasound images in bronchoscopy for peripheral lung lesions

Guan et al., 2024

View HTML
Document ID
9494516948742202827
Author
Guan S
Xu X
Zhu X
Ge Y
Xie J
Zhou J
Publication year
Publication venue
SLAS technology

External Links

Snippet

Traditional imaging methods have limitations in the diagnosis of peripheral lung lesions. The aim of this study is to evaluate the diagnostic value of the distance measurement method based on ultrasound image-based inverted electrostrain (rEBUS) combined with …
Continue reading at www.sciencedirect.com (HTML) (other versions)

Classifications

    • 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
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • 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
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/0059Detecting, measuring or recording for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0062Arrangements for scanning
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10116X-ray image
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/0059Detecting, measuring or recording for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0082Detecting, measuring or recording for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/0031Geometric 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
    • G06T3/0037Reshaping or unfolding a 3D tree structure onto a 2D plane
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/74Details of notification to user or communication with user or patient ; user input means
    • A61B5/742Details of notification to user or communication with user or patient ; user input means using visual displays
    • A61B5/7425Displaying combinations of multiple images regardless of image source, e.g. displaying a reference anatomical image with a live image
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/41Detecting, measuring or recording for evaluating the immune or lymphatic systems
    • A61B5/414Evaluating particular organs or parts of the immune or lymphatic systems
    • A61B5/418Evaluating particular organs or parts of the immune or lymphatic systems lymph vessels, ducts or nodes
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • 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
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A61B10/02Instruments for taking cell samples or for biopsy
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation

Similar Documents

Publication Publication Date Title
Sechopoulos et al. Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art
Pannala et al. Artificial intelligence in gastrointestinal endoscopy
CN112150524B (en) Two-dimensional and three-dimensional medical image registration method and system based on deep learning
Namikawa et al. Utilizing artificial intelligence in endoscopy: a clinician’s guide
Basu et al. RadFormer: Transformers with global–local attention for interpretable and accurate Gallbladder Cancer detection
Lee et al. A straightforward approach to computer-aided polyp detection using a polyp-specific volumetric feature in CT colonography
Hu et al. Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤ 1 cm) based on CT texture analysis
Jiang et al. MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images
Song et al. Hypervascular hepatic focal lesions on dynamic contrast-enhanced CT: preliminary data from arterial phase scans texture analysis for classification
Wang et al. Electromagnetic navigation bronchoscopy combined endobronchial ultrasound in the diagnosis of lung nodules
Ding et al. Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm
Khanna A Review of AI Devices in Cancer Radiology for Breast and Lung Imaging and Diagnosis
Jaramillo et al. Endoscopic ultrasound database of the pancreas
Guan et al. Diagnostic value of rEBUS-TBLB combined distance measurement method based on ultrasound images in bronchoscopy for peripheral lung lesions
Liu et al. Differentiating gastrointestinal stromal tumors from leiomyomas of upper digestive tract using convolutional neural network model by endoscopic ultrasonography
CN111079863B (en) System for identifying focus tissue by utilizing spectral curve statistical fingerprint
Ye et al. Automated segmentation of mass regions in DBT images using a dilated DCNN approach
Zhi et al. Automatic image selection model based on machine learning for endobronchial ultrasound strain elastography videos
Gowda et al. Enhanced Magnetic Resonance Imaging for Accurate Classification of Benign and Malignant Brain Cells
Su et al. The development and validation of pathological sections based U-Net deep learning segmentation model for the detection of esophageal mucosa and squamous cell neoplasm
Tian et al. Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images
van der Stel et al. Size and depth of residual tumor after neoadjuvant chemoradiotherapy in rectal cancer–implications for the development of new imaging modalities for response assessment
Lu et al. Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer
Omer et al. Lung cancer detection using wavelet scattering transform and artificial intelligence technique
Du et al. Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians