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Abstract:
Echocardiographic assessment of systolic and diastolic function of the heart is often limited by image quality. However, the aortic root is well visualized in most patients. We hypothesize that the aortic root motion may correlate with the systolic and diastolic function of the left ventricle of the heart. Data obtained from 101 healthy volunteers (mean age 46.6 ± 12.4) was used in the study. The data contained sequences of standard two-dimensional (2D) echocardiographic B-mode (brightness mode, classical ultrasound grayscale presentation) images corresponding to single cardiac cycles. They also included sets of standard echocardiographic Doppler parameters of the left ventricular systolic and diastolic function. For each B-mode image sequence, the aortic root was tracked with use of a correlation tracking algorithm and systolic and diastolic values of traveled distances and velocities were determined. The aortic root motion parameters were correlated with the standard Doppler parameters used for the assessment of LV function. The aortic root diastolic distance (ARDD) mean value was 1.66 ± 0.26 cm and showed significant, moderate correlation (r up to 0.59, p < 0.0001) with selected left ventricular diastolic Doppler parameters. The aortic root maximal diastolic velocity (ARDV) was 10.8 ± 2.4 cm/s and also correlated (r up to 0.51, p < 0.0001) with some left ventricular diastolic Doppler parameters. The aortic root systolic distance (ARSD) was 1.63 ± 0.19 cm and showed no significant moderate correlation (all r values < 0.40). The aortic root maximal systolic velocity (ARSV) was 9.2 ± 1.6 cm/s and correlated in moderate range only with peak systolic velocity of medial mitral annulus (r = 0.44, p < 0.0001). Based on these results, we conclude, that in healthy subjects, aortic root motion parameters correlate significantly with established measurements of left ventricular function. Aortic root motion parameters can be especially useful in patients with low ultrasound image quality precluding usage of typical LV function parameters.

Abstract:
Purpose
The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound.
Methods
We used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences. The steatosis level of each liver was graded by wedge biopsy. The proposed approach was compared with the hepatorenal index technique and the gray-level co-occurrence matrix algorithm. After the feature extraction, we applied the support vector machine algorithm to classify images containing fatty liver. Based on liver biopsy, the fatty liver was defined to have more than 5% of hepatocytes with steatosis. Next, we used the features and the Lasso regression method to assess the steatosis level.
Results
The area under the receiver operating characteristics curve obtained using the proposed approach was equal to 0.977, being higher than the one obtained with the hepatorenal index method, 0.959, and much higher than in the case of the gray-level co-occurrence matrix algorithm, 0.893. For regression the Spearman correlation coefficients between the steatosis level and the proposed approach, the hepatorenal index and the gray-level co-occurrence matrix algorithm were equal to 0.78, 0.80 and 0.39, respectively.
Conclusions
The proposed approach may help the sonographers automatically diagnose the amount of fat in the liver. The presented approach is efficient and in comparison with other methods does not require the sonographers to select the region of interest.

Keywords:
Nonalcoholic fatty, liver disease, Ultrasound imaging Deep learning, Convolutional neural networks, Hepatorenal index, Transfer learning

Abstract:
Convolutional neural networks (CNNs) have achieved remarkable success in medical image analysis tasks. In ultrasound (US) imaging, CNNs have been applied to object classification, image reconstruction and tissue characterization. However, CNNs can be vulnerable to adversarial attacks, even small perturbations applied to input data may significantly affect model performance and result in wrong output. In this work, we devise a novel adversarial attack, specific to ultrasound (US) imaging. US images are reconstructed based on radio-frequency signals. Since the appearance of US images depends on the applied image reconstruction method, we explore the possibility of fooling deep learning model by perturbing US B-mode image reconstruction method. We apply zeroth order optimization to find small perturbations of image reconstruction parameters, related to attenuation compensation and amplitude compression, which can result in wrong output. We illustrate our approach using a deep learning model developed for fatty liver disease diagnosis, where the proposed adversarial attack achieved success rate of 48%.

Keywords:
adversarial attacks, deep learning, fatty liver, transfer learning, ultrasound imaging