LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection.

The detection of fetal ultrasound standard planes (FUSPs) is important for the diagnosis of fetal malformation and the prevention of perinatal death. As a promising deep-learning technique in FUSP detection, SonoNet's network parameters have a large size. In this paper, we introduced a light pyramid convolution (LPC) block into SonoNet and proposed LPC-SonoNet with reduced network parameters for FUSP detection.

Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver region of interest (ROI) is outlined manually.

Changes in mechanical properties at the muscle level could be detected by Nakagami imaging during in-vivo fixed-end contractions.

In this study, we investigated the capability of the Nakagami transformation to detect changes in vastus lateralis muscle-tendon stiffness (k) during dynamic (and intense) contractions. k was evaluated in eleven healthy males using the gold-standard method (a combination of ultrasound and dynamometric measurements) during maximal and sub-maximal voluntary fixed-end contractions of the knee extensors (20, 40, 60, 80, and 100% of maximum voluntary force), while Nakagami parameters were analysed using the Nakagami transformation during the same contractions. Muscle-belly behaviour was investigated by means of B-mode ultrasound analysis, while Nakagami parameters were obtained in post-processing using radiofrequency data.

Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application.

Background And Objective: Ultrasound information entropy imaging is an emerging quantitative ultrasound technique for characterizing local tissue scatterer concentrations and arrangements. However, the commonly used ultrasound Shannon entropy imaging based on histogram-derived discrete probability estimation suffers from the drawbacks of histogram settings dependence and unknown estimator performance. In this paper, we introduced the information-theoretic cumulative residual entropy (CRE) defined in a continuous distribution of cumulative distribution functions as a new entropy measure of ultrasound backscatter envelope uncertainty or complexity, and proposed ultrasound CRE imaging for tissue characterization.