AI Medical Compendium Topic:
Ultrasonography

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Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images.

Medical ultrasonography
AIM: In this paper we proposed different architectures of convolutional neural network (CNN) to classify fatty liver disease in images using only pixels and diagnosis labels as input. We trained and validated our models using a dataset of 629 images ...

Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver.

Ultrasound in medicine & biology
Quantitative ultrasound (QUS) was used to classify rabbits that were induced to have liver disease by placing them on a fatty diet for a defined duration and/or periodically injecting them with CCl. The ground truth of the liver state was based on li...

Segmentation and classification of thyroid follicular neoplasm using cascaded convolutional neural network.

Physics in medicine and biology
In this paper, we present a segmentation and classification method for thyroid follicular neoplasms based on a combination of the prior-based level set method and deep convolutional neural network. The proposed method aims to discriminate thyroid fol...

Brain Contrast-Enhanced Ultrasound Evaluation of a Pediatric Swine Model.

Ultrasound quarterly
Brain injury remains a leading cause of morbidity and mortality in children. We evaluated the feasibility of using a pediatric swine model to develop contrast-enhanced ultrasound (CEUS)-based measures of brain perfusion for clinical application in va...

Parameter estimation of the homodyned K distribution based on an artificial neural network for ultrasound tissue characterization.

Ultrasonics
The homodyned K (HK) distribution allows a general description of ultrasound backscatter envelope statistics with specific physical meanings. In this study, we proposed a new artificial neural network (ANN) based parameter estimation method of the HK...

Adaptive Ultrasound Beamforming Using Deep Learning.

IEEE transactions on medical imaging
Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal across many applications, spanning from magnetic resonance imaging to ...

Detection and Localization of Ultrasound Scatterers Using Convolutional Neural Networks.

IEEE transactions on medical imaging
Delay-and-sum (DAS) beamforming is unable to identify individual scatterers when their density is so high that their point spread functions overlap. This paper proposes a convolutional neural network (CNN)-based method to detect and localize high-den...

Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features.

Sensors (Basel, Switzerland)
This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neura...

Deep learning segmentation of Primary Sjögren's syndrome affected salivary glands from ultrasonography images.

Computers in biology and medicine
Salivary gland ultrasonography (SGUS) has proven to be a promising tool for diagnosing various diseases manifesting with abnormalities in salivary glands (SGs), including primary Sjögren's syndrome (pSS). At present, the major obstacle for establishi...

A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
It is necessary to monitor the mechanical properties of arteries which directly related to cardiovascular diseases (CVDs) in the early stages. In this study, we proposed a new method based on deep learning (DL) to track the displacement of the vessel...