AIMC Topic: Image Processing, Computer-Assisted

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Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control.

IEEE transactions on visualization and computer graphics
This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method...

Spatio-Temporal Manifold Learning for Human Motions via Long-Horizon Modeling.

IEEE transactions on visualization and computer graphics
Data-driven modeling of human motions is ubiquitous in computer graphics and computer vision applications, such as synthesizing realistic motions or recognizing actions. Recent research has shown that such problems can be approached by learning a nat...

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...

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...

Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accura...