AIMC Topic:
Ultrasonography

Clear Filters Showing 961 to 970 of 1258 articles

Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions.

International journal of computer assisted radiology and surgery
OBJECTIVE: Currently, there is a worldwide shift toward competency-based medical education. This necessitates the use of automated skills assessment methods during self-guided interventions training. Making assessment methods that are transparent and...

Automatic segmentation of levator hiatus from ultrasound images using U-net with dense connections.

Physics in medicine and biology
In this paper, we propose a fully automatic method based on a densely connected convolutional network for the segmentation of the levator hiatus from ultrasound images. A densely connected path is incorporated into a U-net to achieve a deep architect...

Automatic Tracking of Muscle Cross-Sectional Area Using Convolutional Neural Networks with Ultrasound.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: The purpose of this study was to develop an automatic tracking method for the muscle cross-sectional area (CSA) on ultrasound (US) images using a convolutional neural network (CNN). The performance of the proposed method was evaluated and...

Neural networks for automatic scoring of arthritis disease activity on ultrasound images.

RMD open
BACKGROUND: The development of standardised methods for ultrasound (US) scanning and evaluation of synovitis activity by the OMERACT-EULAR Synovitis Scoring (OESS) system is a major step forward in the use of US in the diagnosis and monitoring of pat...

Diagnosis of focal liver lesions from ultrasound using deep learning.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to create an algorithm that simultaneously detects and characterizes (benign vs. malignant) focal liver lesion (FLL) using deep learning.

Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI.

Diagnostic and interventional imaging
PURPOSE: The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ...

Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN.

International journal of computer assisted radiology and surgery
PURPOSE: Ultrasound (US) provides real-time, two-/three-dimensional safe imaging. Due to these capabilities, it is considered a safe alternative to intra-operative fluoroscopy in various computer-assisted orthopedic surgery (CAOS) procedures. However...

Beamforming and Speckle Reduction Using Neural Networks.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
With traditional beamforming methods, ultrasound B-mode images contain speckle noise caused by the random interference of subresolution scatterers. In this paper, we present a framework for using neural networks to beamform ultrasound channel signals...

Training improvements for ultrasound beamforming with deep neural networks.

Physics in medicine and biology
This paper investigates practical considerations of training ultrasound deep neural network (DNN) beamformers. First, we studied training DNNs using the combination of multiple point target responses instead of single point target responses. Next, we...

Quantifying lung ultrasound comets with a convolutional neural network: Initial clinical results.

Computers in biology and medicine
Lung ultrasound comets are "comet-tail" artifacts appearing in lung ultrasound images. They are particularly useful in detecting several lung pathologies and may indicate the amount of extravascular lung water. However, the comets are not always well...