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

Niemann et al., 2020 - Google Patents

Interactive exploration of a 3D intracranial aneurysm wall model extracted from histologic slices

Niemann et al., 2020

View PDF
Document ID
4278748888756535346
Author
Niemann A
Weigand S
Hoffmann T
Skalej M
Tulamo R
Preim B
Saalfeld S
Publication year
Publication venue
International Journal of Computer Assisted Radiology and Surgery

External Links

Snippet

Purpose Currently no detailed in vivo imaging of the intracranial vessel wall exists. Ex vivo histologic images can provide information about the intracranial aneurysm (IA) wall composition that is useful for the understanding of IA development and rupture risk. For a 3D …
Continue reading at source.ymvkt.cn (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
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
    • 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
    • G06T2207/10072—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
    • G06F19/34—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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • 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
    • G06T2207/10056—Microscopic image
    • 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
    • G06T2207/30172—Centreline of tubular or elongated structure
    • 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
    • G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • 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
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • 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
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/24—Indexing scheme for image data processing or generation, in general involving graphical user interfaces [GUIs]

Similar Documents

Publication Publication Date Title
Zeiser et al. Segmentation of masses on mammograms using data augmentation and deep learning
Shum et al. Semiautomatic vessel wall detection and quantification of wall thickness in computed tomography images of human abdominal aortic aneurysms
WO2010115885A1 (en) Predictive classifier score for cancer patient outcome
Pinto et al. Enhancing clinical MRI perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Nowak et al. End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT
Tibermacine et al. Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches
Brutti et al. Deep learning to automatically segment and analyze abdominal aortic aneurysm from computed tomography angiography
Liu et al. Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: influence of features number on logistic regression model performance
WO2022258718A1 (en) Processing multimodal images of tissue for medical evaluation
Kaliyugarasan et al. Pulmonary nodule classification in lung cancer from 3D thoracic CT scans using fastai and MONAI
Bhattacharjee et al. Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI
Orton et al. Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: Insights from the TRACERx Renal study
Niemann et al. Interactive exploration of a 3D intracranial aneurysm wall model extracted from histologic slices
Robinson et al. Correlative 3D imaging and microfluidic modelling of human pulmonary lymphatics using immunohistochemistry and high-resolution μCT
Cho et al. Affinity graph based end-to-end deep convolutional networks for ct hemorrhage segmentation
Wang et al. Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images
CN111528918B (en) Tumor volume change trend graph generation device after ablation, equipment and storage medium
KR20230027164A (en) Systems and methods for processing electronic images to generate tissue map visualizations
Mudeng et al. Domain and histopathology adaptations–based classification for Malignancy Grading System
Häberle et al. Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound
Garau et al. A segmentation tool for pulmonary nodules in lung cancer screening: Testing and clinical usage
Schenone et al. Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma
Bhushan Liver cancer detection using hybrid approach-based convolutional neural network (HABCNN)
Nowakowska et al. Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI
US20220375077A1 (en) Method for generating models to automatically classify medical or veterinary images derived from original images into at least one class of interest