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

Chlap et al., 2021 - Google Patents

A review of medical image data augmentation techniques for deep learning applications

Chlap et al., 2021

Document ID
11885254696629287118
Author
Chlap P
Min H
Vandenberg N
Dowling J
Holloway L
Haworth A
Publication year
Publication venue
Journal of Medical Imaging and Radiation Oncology

External Links

Snippet

Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional …
Continue reading at onlinelibrary.wiley.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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • 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
    • 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
    • G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • 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
    • 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
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/007—Dynamic range modification
    • G06T5/008—Local, e.g. shadow enhancement
    • 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

Similar Documents

Publication Publication Date Title
Chlap et al. A review of medical image data augmentation techniques for deep learning applications
Sorin et al. Creating artificial images for radiology applications using generative adversarial networks (GANs)–a systematic review
Eslami et al. Image-to-images translation for multi-task organ segmentation and bone suppression in chest x-ray radiography
Hu et al. Bidirectional mapping generative adversarial networks for brain MR to PET synthesis
Mazurowski et al. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Malhotra et al. [Retracted] Deep Neural Networks for Medical Image Segmentation
Liu et al. Automatic prostate zonal segmentation using fully convolutional network with feature pyramid attention
Fu et al. LungRegNet: an unsupervised deformable image registration method for 4D‐CT lung
Wang et al. 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis
Men et al. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks
Zhou et al. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method
Zheng et al. Automatic liver tumor segmentation on dynamic contrast enhanced MRI using 4D information: deep learning model based on 3D convolution and convolutional LSTM
Liu et al. CT‐based multi‐organ segmentation using a 3D self‐attention U‐net network for pancreatic radiotherapy
Al-Masni et al. CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation
Koshino et al. Narrative review of generative adversarial networks in medical and molecular imaging
Jansen et al. Liver segmentation and metastases detection in MR images using convolutional neural networks
Amirrajab et al. Label-informed cardiac magnetic resonance image synthesis through conditional generative adversarial networks
Wang et al. MDU‐Net: A Convolutional Network for Clavicle and Rib Segmentation from a Chest Radiograph
Jung et al. Deep learning for medical image analysis: Applications to computed tomography and magnetic resonance imaging
Chen et al. Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review
Li et al. Large-kernel attention for 3D medical image segmentation
Marin et al. Deep learning-based GTV contouring modeling inter-and intra-observer variability in sarcomas
Chen et al. DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT
Chen et al. Dual-branch squeeze-fusion-excitation module for cross-modality registration of cardiac SPECT and CT
Wang et al. DMCT-Net: dual modules convolution transformer network for head and neck tumor segmentation in PET/CT