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

Zhang et al., 2020 - Google Patents

A multiple‐channel and atrous convolution network for ultrasound image segmentation

Zhang et al., 2020

Document ID
9254292456806279467
Author
Zhang L
Zhang J
Li Z
Song Y
Publication year
Publication venue
Medical Physics

External Links

Snippet

Purpose Ultrasound image segmentation is a challenging task due to a low signal‐to‐noise ratio and poor image quality. Although several approaches based on the convolutional neural network (CNN) have been applied to ultrasound image segmentation, they have …
Continue reading at aapm.onlinelibrary.wiley.com (other versions)

Classifications

    • 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
    • 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
    • G06F19/34Computer-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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • 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
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design

Similar Documents

Publication Publication Date Title
Altaf et al. Going deep in medical image analysis: concepts, methods, challenges, and future directions
Zhang et al. A multiple‐channel and atrous convolution network for ultrasound image segmentation
Nazir et al. OFF-eNET: An optimally fused fully end-to-end network for automatic dense volumetric 3D intracranial blood vessels segmentation
Zhou et al. Artificial intelligence in medical imaging of the liver
Vu et al. Evaluation of multislice inputs to convolutional neural networks for medical image segmentation
Zhou et al. LAEDNet: a lightweight attention encoder–decoder network for ultrasound medical image segmentation
Lyu et al. AMS-PAN: Breast ultrasound image segmentation model combining attention mechanism and multi-scale features
Han et al. Automatic segmentation of human placenta images with U-Net
Lin et al. Variance‐aware attention U‐Net for multi‐organ segmentation
Chen et al. A novel convolutional neural network for kidney ultrasound images segmentation
Sun et al. Classification for thyroid nodule using ViT with contrastive learning in ultrasound images
Nizamani et al. Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data
Yang et al. Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation
Tang et al. Introducing frequency representation into convolution neural networks for medical image segmentation via twin-Kernel Fourier convolution
Pandey et al. A literature review on application of machine learning techniques in pancreas segmentation
Ojika et al. Addressing the memory bottleneck in AI model training
CN109727227A (en) A kind of diagnosis of thyroid illness method based on SPECT image
Xia et al. A nested parallel multiscale convolution for cerebrovascular segmentation
Khan et al. Transformers in medical image segmentation: a narrative review
Chang et al. Segmentation of brain MR images using a charged fluid model
Liu et al. CAM‐Wnet: An effective solution for accurate pulmonary embolism segmentation
Karri et al. SGC-ARANet: scale-wise global contextual axile reverse attention network for automatic brain tumor segmentation
Zhang et al. DARN: Deep attentive refinement network for liver tumor segmentation from 3D CT volume
Zhu et al. 3D pyramid pooling network for abdominal MRI series classification
Raina et al. Deep Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images using Multi-scale and Higher-order Processing