AIMC Topic: Image Processing, Computer-Assisted

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Active Inference and Deep Generative Modeling for Cognitive Ultrasound.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultraportable and cost-effective, akin to the stethoscope. Nevertheless, and despite many advances, US image quality and diagnostic...

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for lar...

Boosting Cardiac Color Doppler Frame Rates With Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ...

BEAS-Net: A Shape-Prior-Based Deep Convolutional Neural Network for Robust Left Ventricular Segmentation in 2-D Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently...

Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme using physics-informed neural networks (PINNs) and a ...

Hardware-Independent Deep Signal Processing: A Feasibility Study in Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning (DL) models have emerged as alternative methods to conventional ultrasound (US) signal processing, offering the potential to mimic signal processing chains, reduce inference time, and enable the portability of processing chains across h...

Deep-Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images Using Multiscale and Higher-Order Processing.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Autonomous ultrasound image quality assessment (US-IQA) is a promising tool to aid the interpretation by practicing sonographers and to enable the future robotization of ultrasound procedures. However, autonomous US-IQA has several challenges. Ultras...

Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI.

Medical image analysis
In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge li...

Strip and boundary detection multi-task learning network for segmentation of meibomian glands.

Medical physics
BACKGROUND: Automatic segmentation of meibomian glands in near-infrared meibography images is basis of morphological parameter analysis, which plays a crucial role in facilitating the diagnosis of meibomian gland dysfunction (MGD). The special strip ...

The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction.

Medical image analysis
The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been developed to extract high-quality cortical surfaces from structural brain ...