AIMC Topic: Image Processing, Computer-Assisted

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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...

Displacement Estimation in Ultrasound Elastography Using Pyramidal Convolutional Neural Network.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
In this article, two novel deep learning methods are proposed for displacement estimation in ultrasound elastography (USE). Although convolutional neural networks (CNNs) have been very successful for displacement estimation in computer vision, they h...

A Deep Learning Approach to Photoacoustic Wavefront Localization in Deep-Tissue Medium.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Optical photons undergo strong scattering when propagating beyond 1-mm deep inside biological tissue. Finding the origin of these diffused optical wavefronts is a challenging task. Breaking through the optical diffusion limit, photoacoustic (PA) imag...

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation.

Sensors (Basel, Switzerland)
Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with...

Deep convolution neural networks based artifact suppression in under-sampled radial acquisitions of myocardial T mapping images.

Physics in medicine and biology
We developed a deep convolutional neural network (CNN) based method to remove streaking artefact from accelerated radial acquisitions of myocardial T -mapping images. A deep CNN based on a modified U-Net architecture was developed and trained to remo...

A convolutional neural network-based anthropomorphic model observer for signal-known-statistically and background-known-statistically detection tasks.

Physics in medicine and biology
The purpose of this study is implementation of an anthropomorphic model observer using a convolutional neural network (CNN) for signal-known-statistically (SKS) and background-known-statistically (BKS) detection tasks. We conduct SKS/BKS detection ta...

Artificial intelligence in image reconstruction: The change is here.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and f...

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.

ACS nano
Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for l...

ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The use of l (p = 1,2) norms has largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess the loss of visual information, these simple norms are not very consistent w...

Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging.

Medical physics
PURPOSE: Post-reconstruction filtering is often applied for noise suppression due to limited data counts in myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT). We study a deep learning (DL) approach for denoisi...