[go: up one dir, main page]

Gençtürk et al., 2025 - Google Patents

Artificial intelligence and computed tomography imaging for midline shift detection

Gençtürk et al., 2025

Document ID
6844815719667650018
Author
Gençtürk T
Gülağız F
Kaya
Publication year
Publication venue
The European Physical Journal Special Topics

External Links

Snippet

Midline shift (MLS) is a serious, potentially life-threatening displacement of brain structures. As in other medical fields, there is a need for computer-aided diagnostic systems to support rapid and accurate diagnosis in the detection of MLS. In this study, a comprehensive …
Continue reading at link.springer.com (other versions)

Classifications

    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • 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
    • G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
    • 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
    • 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
    • G06F19/321—Management of medical image data, e.g. communication or archiving systems such as picture archiving and communication systems [PACS] or related medical protocols such as digital imaging and communications in medicine protocol [DICOM]; Editing of medical image data, e.g. adding diagnosis information
    • 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
    • G06F19/3487—Medical report generation
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray 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
    • G06T2207/20—Special algorithmic 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA 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
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Health care, e.g. hospitals; Social work
    • 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

Similar Documents

Publication Publication Date Title
Bakas et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features
Lesjak et al. A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus
Ito et al. A comparison of automated lesion segmentation approaches for chronic stroke T1‐weighted MRI data
Brugnara et al. Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis
Sarmento et al. An IoT platform for the analysis of brain CT images based on Parzen analysis
Huang et al. Medical media analytics via ranking and big learning: A multi-modality image-based disease severity prediction study
Bansal et al. An improved hybrid classification of brain tumor MRI images based on conglomeration feature extraction techniques
Moon et al. Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images
Ong et al. Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy
Kaur et al. A New Deep Learning Framework for Accurate Intracranial Brain Hemorrhage Detection and Classification Using Real-Time Collected NCCT Images
Prakasam Spatial attention-based CSR-Unet framework for subdural and epidural hemorrhage segmentation and classification using CT images
Zhang et al. Deep learning and radiomics based automatic diagnosis of hippocampal sclerosis
Phan et al. Automatic detection and classification of brain hemorrhages
Gudadhe et al. Multivariate analysis of ischaemic lesions using Computed Tomography and CT Perfusion Imaging: Critical review
Saher et al. Automated grading system for breast cancer histopathological images using Histogram of Oriented Gradients (HOG) algorithm
Liu et al. Dynamic graph neural representation based multi-modal fusion model for cognitive outcome prediction in stroke cases
Gençtürk et al. Artificial intelligence and computed tomography imaging for midline shift detection
Li et al. Multi-instance learning based on spatial continuous category representation for case-level meningioma grading in MRI images
Orzan et al. Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis–a systematic review
Zhang et al. Region of interest discovery using discriminative concrete autoencoder for covid-19 lung ct images
Vidyarthi A dense-layered deep neural model-based classification of brain hemorrhages using head computer tomography images
Mehrtash et al. Evaluation of mediastinal lymph node segmentation of heterogeneous CT data with full and weak supervision
Chillar et al. Role of Sustainable Strategies Using Artifical Inteligence in Brain Tumour Detection and Treatment
Danala et al. Developing interactive computer-aided detection tools to support translational clinical research
Çay Deep learning-based brain tumor segmentation: a comparison of U-Net and segNet algorithms