KEYWORDS: Ultrasonography, Statistical analysis, Education and training, Deep learning, Data modeling, Tissues, Point spread functions, Measurement uncertainty, Backscatter, Scattering
Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure. Among different QUS parameters, scatterer number density has been found to be a reliable biomarker to detect different abnormalities. The homodyned K-distribution (HK-distribution) is a model for the probability density function of the ultrasound echo amplitude that can model different scattering scenarios but requires a large number of samples to be estimated reliably. Parametric images of HK-distribution parameters can be formed by dividing the envelope data into small overlapping patches and estimating parameters within the patches independently. This approach imposes two limiting constraints: the HK-distribution parameters are assumed to be constant within each patch, and each patch requires enough independent samples. In order to mitigate those problems, we employ a deep learning approach to estimate parametric images of scatterer number density (related to HK-distribution shape parameter) without patching. Furthermore, an uncertainty map of the network’s prediction is quantified to provide insight about the confidence of the network about the estimated HK parameter values.
Recently, Convolutional Neural Networks (CNNs) have been very successful in optical flow estimation in computer vision. UltraSound Elastography (USE) displacement estimation step can be performed by optical flow CNNs. However, there is a large domain gap between ultrasound Radio-Frequency (RF) data and computer vision images which reduces the overall accuracy of displacement estimation. Some modifications of the network architecture are required to be able to extract reliable information from RF data. Modified Pyramidal Network (MPWC-Net) which is based on the well-known PWC-Net was among the first attempts that adopts the optical flow CNNs to USE displacement estimation. However, MPWC-Net suffers from several shortcomings that limit its application especially for unsupervised training. In this paper, we propose additional modifications to substantially improve MPWC-Net. We also publicly released the network’s trained weights.
Ultrasound (US) is a low-cost, portable, and safe tool for breast cancer screening. However, automatic classification of invasive ductal carcinoma (IDC) in US is a difficult classification task due to their similar appearance to fibroadenoma (FA) (a type of benign tumor). Another challenge is the limited availability of US data with ground truth labels, further complicating the adoption of deep learning techniques for IDC detection. It has been shown that deep classification networks perform better when they simultaneously learn multiple correlated tasks. However, most previous studies on breast US classifications focused on the binary classification of benign versus malignant tumors. To this end, we propose a multi-class classification deep learning-based strategy mainly focusing on the classification of IDC. Inspired by multi-task learning (MTL), we adopt a novel scheme in adding the background tissue as an additional class and show substantial improvements in IDC detection.
UltraSound Elastography (USE) has been widely used to obtain mechanical properties of tissues. Radio frequency (RF) data is usually used in USE to estimate the displacement. However, RF data is not available in all ultrasound imaging devices. B-mode images which are basically the envelope of the RF data are the most well-known output of the common ultrasound imaging devices. In B-mode images, the phase information of RF data is lost. Consequently, USE can be more challenging and the strain image quality would be degraded. The aim of this paper is to employ Demons algorithm, which is a powerful non-rigid image registration algorithm, to estimate displacement using B-mode images. In USE, the post-compression image may have large deformations in axial direction which deteriorates the Demons algorithm performance. In order to compensate the large deformations, an optimization algorithm is proposed to find and compensate the mean value axial deformation. Experimental and numerical phantoms are used to verify the algorithm performance in normal and severe situations. The results are compared with the common normalized cross correlation (NCC) algorithm. The results confirm that Demons algorithm is an appropriate algorithm for USE for B-mode images considering the fact that phase information are not available.
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