AIMC Topic: Humans

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Fine-Tuning U-Net for Ultrasound Image Segmentation: Different Layers, Different Outcomes.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
One way of resolving the problem of scarce and expensive data in deep learning for medical applications is using transfer learning and fine-tuning a network which has been trained on a large data set. The common practice in transfer learning is to ke...

LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semiautomatically in clinical routine and is, thus, prone to interobserver and intraobserver variabilities. R...

A Deep Learning Approach to Resolve Aliasing Artifacts in Ultrasound Color Flow Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Despite being used clinically as a noninvasive flow visualization tool, color flow imaging (CFI) is known to be prone to aliasing artifacts that arise due to fast blood flow beyond the detectable limit. From a visualization standpoint, these aliasing...

Estimation of End-Diastole in Cardiac Spectral Doppler Using Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Electrocardiogram (ECG) is often used together with a spectral Doppler ultrasound to separate heart cycles by determining the end-diastole locations. However, the ECG signal is not always recorded. In such cases, the cardiac cycles can be estimated m...

Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Single plane wave transmissions are promising for automated imaging tasks requiring high ultrasound frame rates over an extended field of view. However, a single plane wave insonification typically produces suboptimal image quality. To address this l...

Synthetic Elastography Using B-Mode Ultrasound Through a Deep Fully Convolutional Neural Network.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently developed, advanced technique assesses the speed of a laterally traveling shear wave after an acoustic radiation fo...

CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep fully connected networks are often considered "universal approximators" that are capable of learning any function. In this article, we utilize this particular property of deep neural networks (DNNs) to estimate normalized cross correlation as a ...

Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Volume and ejection fraction (EF) measurements of the left ventricle (LV) in 2-D echocardiography are associated with a high uncertainty not only due to interobserver variability of the manual measurement, but also due to ultrasound acquisition error...

A Single-Shot Region-Adaptive Network for Myotendinous Junction Segmentation in Muscular Ultrasound Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Tracking the myotendinous junction (MTJ) in consecutive ultrasound images is crucial for understanding the mechanics and pathological conditions of the muscle-tendon unit. However, the lack of reliable and efficient identification of MTJ due to poor ...

A Pilot Study on Convolutional Neural Networks for Motion Estimation From Ultrasound Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
In recent years, deep learning (DL) has been successfully applied to the analysis and processing of ultrasound images. To date, most of this research has focused on segmentation and view recognition. This article benchmarks different convolutional ne...