A survey on deep learning in medical image analysis

We present here the first update of the 2013 EFSUMB (European Federation of Societies for Ultrasound in Medicine and Biology) Guidelines and Recommendations on the clinical use of elastography with a focus on the assessment of diffuse liver disease. The short version provides clinical information about the practical use of elastography equipment and interpretation of results in the assessment of diffuse liver disease and analyzes the main findings based on published studies, stressing the evidence from meta-analyses. The role of elastography in different etiologies of liver disease and in several clinical scenarios is also discussed. All of the recommendations are judged with regard to their evidence-based strength according to the Oxford Centre for Evidence-Based Medicine Levels of Evidence. This updated document is intended to act as a reference and to provide a practical guide for both beginners and advanced clinical users.

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EFSUMB Guidelines and Recommendations on the Clinical Use of Liver Ultrasound Elastography, Update 2017 (Long Version)

Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

Wearable Photoplethysmographic Sensors—Past and Present

https://hal.inria.fr/hal-01648559/document

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.

Deep Learning in Medical Ultrasound Analysis: A Review

Deep learning based classification of breast tumors with shear-wave elastography.

Bioinspired in-sensor visual adaptation for accurate perception

Low frequency ultrasound (<1 MHz) has been demonstrated to be a promising approach for non-invasive neuro-stimulation. However, the focal width is limited to be half centimeter scale. Minimizing the stimulation region with higher frequency ultrasound will provide a great opportunity to expand its application. This study first time examines the feasibility of using high frequency (5 MHz) ultrasound to achieve neuro-stimulation in brain and verifies the anatomical specificity of neuro-stimulation in vivo. 1 MHz and 5 MHz ultrasound stimulation were evaluated in the same group of mice. Electromyography (EMG) collected from tail muscles together with the motion response videos were analyzed for evaluating the stimulation effects. Our results indicate that 5 MHz ultrasound can successfully achieve neuro-stimulation. The equivalent diameter (ED) of the stimulation region with 5 MHz ultrasound (0.29 ± 0.08 mm) is significantly smaller than that with 1 MHz (0.83 ± 0.11 mm). The response latency of 5 MHz ultrasound (45 ± 31 ms) is also shorter than that of 1 MHz ultrasound (208 ± 111 ms). Consequently, high frequency (5 MHz) ultrasound can successfully activate the brain circuits in mice. It provides a smaller stimulation region, which offers improved anatomical specificity for neuro-stimulation in a non-invasive manner.

/pdf/improved-anatomical-specificity-of-non-invasive-neuro-4lu1ggc106.pdf

Improved Anatomical Specificity of Non-invasive Neuro-stimulation by High Frequency (5 MHz) Ultrasound

The purpose of this study was to determine the measurement depth range within which liver stiffness can be reliably assessed using real-time shear wave elastography (SWE) technology. Measurements were performed on phantoms and healthy volunteers. In the first group of patients, measurements were performed at depths of 2–8 cm from the probe surface. In the second group of patients, measurements were conducted 0–7 cm below the liver capsule. Success rate of measurements (SRoM), success rate of patients (SRoS) and coefficients of variation (CVs) of repeated measurements were compared. The SRoMs at 3–7 cm and the CVs at 2–5 cm from the probe surface were significantly higher and lower than those at other depths ( p

Influence of measurement depth on the stiffness assessment of healthy liver with real-time shear wave elastography.

Ultrasound shear-wave elastography (SWE) has become a valuable tool for diagnosis of breast tumors. The purpose of this study was to quantify the elastic heterogeneity of breast tumors in SWE by using contourlet-based texture features and evaluating their diagnostic performance for classification of benign and malignant breast tumors, with pathologic results as the gold standard. A total of 161 breast tumors in 125 women who underwent B-mode and SWE ultrasonography before biopsy were included. Five quantitative texture features in SWE images were extracted from the directional subbands after the contourlet transform, including the mean (Tmean), maximum (Tmax), median (Tmed), third quartile (Tqt), and standard deviation (Tsd) of the subbands. Diagnostic performance of the texture features and the classic features was compared using the area under the receiver operating characteristic curve (AUC) and the leave-one-out cross validation with Fisher classifier. The feature Tmean achieved the highest AUC (0.968) among all features and it yielded a sensitivity of 89.1%, a specificity of 94.3% and an accuracy of 92.5% for differentiation between benign and malignant tumors via the leave-one-out cross validation. Compared with the best classic feature, i.e., the maximum elasticity, Tmean improved the AUC, sensitivity, specificity and accuracy by 3.5%, 12.7%, 2.8% and 6.2%, respectively. The Tmed, Tqt and Tsd were also superior to the classic features in terms of the AUC and accuracy. The results demonstrated that the contourlet-based texture features captured the tumor's elastic heterogeneity and improved diagnostic performance contrasted with the classic features.

Congzhi Wang

Papers

Deep learning based classification of breast tumors with shear-wave elastography.

Bioinspired in-sensor visual adaptation for accurate perception

Improved Anatomical Specificity of Non-invasive Neuro-stimulation by High Frequency (5 MHz) Ultrasound

Influence of measurement depth on the stiffness assessment of healthy liver with real-time shear wave elastography.

Quantification of elastic heterogeneity using contourlet-based texture analysis in shear-wave elastography for breast tumor classification.