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

Lee et al., 2020 - Google Patents

Study on Optimal Generative Network for Synthesizing Brain Tumor‐Segmented MR Images

Lee et al., 2020

View PDF @Full View
Document ID
3186046297680680377
Author
Lee H
Jo J
Lim H
Publication year
Publication venue
Mathematical Problems in Engineering

External Links

Snippet

Due to institutional and privacy issues, medical imaging researches are confronted with serious data scarcity. Image synthesis using generative adversarial networks provides a generic solution to the lack of medical imaging data. We synthesize high‐quality brain tumor …
Continue reading at onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • 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
    • G06T2207/10084Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • 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
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G06F19/321Management 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
    • 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
    • G06T2207/20Special algorithmic details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • G06T11/003Reconstruction from projections, e.g. tomography
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image

Similar Documents

Publication Publication Date Title
Sorin et al. Creating artificial images for radiology applications using generative adversarial networks (GANs)–a systematic review
Shen et al. Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning
Pan et al. 2D medical image synthesis using transformer-based denoising diffusion probabilistic model
Ouyang et al. Ultra‐low‐dose PET reconstruction using generative adversarial network with feature matching and task‐specific perceptual loss
Wang et al. 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis
Emami et al. Generating synthetic CTs from magnetic resonance images using generative adversarial networks
US11132792B2 (en) Cross domain medical image segmentation
Hu et al. Brain MR to PET synthesis via bidirectional generative adversarial network
Wolterink et al. Deep MR to CT synthesis using unpaired data
Yu et al. Medical image synthesis via deep learning
Dayarathna et al. Deep learning based synthesis of MRI, CT and PET: Review and analysis
Fei et al. Deep learning‐based multi‐modal computing with feature disentanglement for MRI image synthesis
Chen et al. Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review
Sun et al. Double U-Net CycleGAN for 3D MR to CT image synthesis
Lee et al. Study on Optimal Generative Network for Synthesizing Brain Tumor‐Segmented MR Images
CN111340903A (en) Method and system for generating synthetic PET-CT image based on non-attenuation correction PET image
CN112819914A (en) PET image processing method
Amirkolaee et al. Development of a GAN architecture based on integrating global and local information for paired and unpaired medical image translation
Wang et al. 3D multi-modality Transformer-GAN for high-quality PET reconstruction
Poonkodi et al. 3D-MedTranCSGAN: 3D medical image transformation using CSGAN
Mangalagiri et al. Toward generating synthetic CT volumes using a 3D-conditional generative adversarial network
Amirkolaee et al. Medical image translation using an edge-guided generative adversarial network with global-to-local feature fusion
Han et al. An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis
Santini et al. Unpaired PET/CT image synthesis of liver region using CycleGAN
Prakash et al. Decoupled SculptorGAN Framework for 3D Reconstruction and Enhanced Segmentation of Kidney Tumors in CT Images