Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature

Arterial pulse wave velocity with tissue doppler imaging

This paper describes a new noninvasive ultrasonic method for estimating pulse wave velocity (PWV), an important physical parameter for characterizing the elastic properties of the arterial walls. The method utilizes a relatively new color Doppler modality for measuring tissue motion (tissue Doppler imaging or TDI). In contrast to previously proposed methods, the TDI modality offers multiple recording sites along the artery that improve the PWV estimation considerably. The new PWV estimation method was evaluated through an in vitro setup consisting of an elastic vessel supplied with a pulsatile pump. The study concentrated on the effect of different system parameters controlling resolution, sensitivity and the amount of acquired data. It was shown that the system parameters have a significant effect on the PWV variance, whereas the PWV mean remains unchanged. It was also established that high temporal resolution is the most vital parameter for minimizing PWV variance. Finally, the new PWV estimation method was applied to a limited set of human carotid artery data sets, with good results. (E-mail:[email protected]

A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography

The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77–0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists’ diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination. © 2017 World Federation for Ultrasound in Medicine & Biolog

A new method for the noninvasive determination of abdominal muscle feedforward activity based on tissue velocity information from tissue Doppler imaging

Rapid arm movements elicit anticipatory activation of the deep-lying abdominal muscles; this appears modified in back pain, but the invasive technique used for its assessment [fine-wire electromyography (EMG)] has precluded its widespread investigation. We examined whether tissue-velocity changes recorded with ultrasound (M-mode) tissue Doppler imaging (TDI) provided a viable noninvasive alternative. Fourteen healthy subjects rapidly flexed, extended, and abducted the shoulder; recordings were made of medial deltoid (MD) surface EMG and of fine-wire EMG and TDI tissue-velocity changes of the contralateral transversus abdominis, obliquus internus, and obliquus externus. Muscle onsets were determined by blinded visual analysis of EMG and TDI data. TDI could not distinguish between the relative activation of the three muscles, so in subsequent analyses only the onset of the earliest abdominal muscle activity was used. The latter occurred <50 ms after the onset of medial deltoid EMG (i.e., was feedforward) and correlated with the corresponding EMG onsets (r = 0.47, P < 0.0001). The mean difference between methods was 20 ms and was likely explained by electromechanical delay; limits of agreement were wide (-40 to +80 ms) but no greater than those typical of repeated measurements using either technique. The between-day standard error of measurement of the TDI onsets (examined in 16 further subjects) was 16 ms. TDI yielded reliable and valid measures of the earliest onset of feedforward activity within the anterolateral abdominal muscle group. The method can be used to assess muscle dysfunction in large groups of back-pain patients and may also be suitable for the noninvasive analysis of other deep-lying or small/thin muscles