AI Medical Compendium Topic:
Ultrasonography

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Super-Resolution Ultrasound Localization Microscopy Through Deep Learning.

IEEE transactions on medical imaging
Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with signi...

Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound. Recently, generative adversarial network ...

Ultrasound image reconstruction from plane wave radio-frequency data by self-supervised deep neural network.

Medical image analysis
Image reconstruction from radio-frequency (RF) data is crucial for ultrafast plane wave ultrasound (PWUS) imaging. Compared with the traditional delay-and-sum (DAS) method based on relatively imprecise assumptions, sparse regularization (SR) method d...

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Ultrasonic imaging
Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis c...

Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
The highly complementary information provided by multispectral optoacoustics and pulse-echo ultrasound have recently prompted development of hybrid imaging instruments bringing together the unique contrast advantages of both modalities. In the hybrid...

Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images.

Nature communications
It is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural area without relevant expertise. To help diagnose BA based on sonographic gallbladder images, an ensembled deep learn...

Deep virtual adversarial self-training with consistency regularization for semi-supervised medical image classification.

Medical image analysis
Convolutional neural networks have achieved prominent success on a variety of medical imaging tasks when a large amount of labeled training data is available. However, the acquisition of expert annotations for medical data is usually expensive and ti...

Automatic segmentation and location learning of neonatal cerebral ventricles in 3D ultrasound data combining CNN and CPPN.

Computers in biology and medicine
Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS). This condition can develop into life-threatening hydrocephalus and is correlated with future neuro-developmental impairments. Con...

Learning to map 2D ultrasound images into 3D space with minimal human annotation.

Medical image analysis
In fetal neurosonography, aligning two-dimensional (2D) ultrasound scans to their corresponding plane in the three-dimensional (3D) space remains a challenging task. In this paper, we propose a convolutional neural network that predicts the position ...

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

The international journal of cardiovascular imaging
Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have f...