patents.google.com

Standvoss et al., 2018 - Google Patents

  • ️Mon Jan 01 2018
Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional neural networks

Standvoss et al., 2018

View PDF
Document ID
7975082470847518849
Author
Crijns T
Goerke L
Janssen D
Kern S
van Niedek T
van Vugt J
Burgos N
Gerritse E
Mol J
van de Vooren D
Ghafoorian M
van den Heuvel T
Manniesing R
Publication year
2018
Publication venue
Medical imaging 2018: computer-aided diagnosis

External Links

Snippet

The number and location of cerebral microbleeds (CMBs) in patients with traumatic brain injury (TBI) is important to determine the severity of trauma and may hold prognostic value for patient outcome. However, manual assessment is subjective and time-consuming due to …

Continue reading at www.researchgate.net (PDF) (other versions)
  • 206010070976 Craniocerebral injury 0 title abstract description 26

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
    • 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
    • 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
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
    • 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
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • 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
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Jnawali et al. 2018 Deep 3D convolution neural network for CT brain hemorrhage classification
Nakao et al. 2018 Deep neural network‐based computer‐assisted detection of cerebral aneurysms in MR angiography
Mehrtash et al. 2017 Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks
Park et al. 2016 Colonoscopic polyp detection using convolutional neural networks
Atlason et al. 2019 Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder
Yap et al. 2018 End-to-end breast ultrasound lesions recognition with a deep learning approach
Standvoss et al. 2018 Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional neural networks
Chudzik et al. 2018 Exudate segmentation using fully convolutional neural networks and inception modules
Sathyan et al. 2018 Lung nodule classification using deep ConvNets on CT images
Salami et al. 2022 Designing a clinical decision support system for Alzheimer’s diagnosis on OASIS-3 data set
Perdomo et al. 2017 Convolutional network to detect exudates in eye fundus images of diabetic subjects
Chudzik et al. 2018 Microaneurysm detection using deep learning and interleaved freezing
Jadon et al. 2020 A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial
Li et al. 2017 Discriminating between benign and malignant breast tumors using 3D convolutional neural network in dynamic contrast enhanced-MR images
Mathai et al. 2022 Lymph node detection in T2 MRI with transformers
Meijs et al. 2018 Artery and vein segmentation of the cerebral vasculature in 4D CT using a 3D fully convolutional neural network
Li et al. 2018 Blind CT image quality assessment via deep learning strategy: initial study
Latif et al. 2021 Digital forensics use case for glaucoma detection using transfer learning based on deep convolutional neural networks
Kobashi et al. 2006 Computer-aided diagnosis of intracranial aneurysms in MRA images with case-based reasoning
El-Ateif et al. 2024 Eye diseases diagnosis using deep learning and multimodal medical eye imaging
Anand et al. 2023 Automated classification of intravenous contrast enhancement phase of ct scans using residual networks
Li et al. 2021 Longitudinal subcortical segmentation with deep learning
Xue et al. 2018 Using deep learning for detecting gender in adult chest radiographs
Goyal et al. 2022 Automated kidney segmentation by mask R-CNN in T2-weighted magnetic resonance imaging
Duran et al. 2022 Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI