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Machine learning and ultrasound

Francesco Raimondi, Fiorella Migliaro, Luisa Verdoliva, Diego Gragnaniello, Giovanni Poggi, Roberta Kosova, Carlo Sansone, Gianfranco Vallone, Letizia Capasso
BACKGROUND AND AIM: Lung ultrasound has been used to describe common respiratory diseases both by visual and computer-assisted gray scale analysis. In the present paper, we compare both methods in assessing neonatal respiratory status keeping two oxygenation indexes as standards. PATIENTS AND METHODS: Neonates admitted to the NICU for respiratory distress were enrolled. Two neonatologists not attending the patients performed a lung scan, built a single frame database and rated the images with a standardized score...
2018: PloS One
Bukweon Kim, Kang Cheol Kim, Yejin Park, Ja-Young Kwon, Jaeseong Jang, Jin Keun Seo
Obstetricians mainly use ultrasound imaging for fetal biometric measurement. However, such measurement is cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated owing to the patient-specific, operator-dependent, and machine-specific characteristics of such images. This paper proposes a method for the automatic fetal biometry estimation from 2D ultrasound data through several processes consisting of a specially designed convolutional neural network (CNN) and U-Net for each process...
September 18, 2018: Physiological Measurement
Wei Li, Yang Huang, Bo-Wen Zhuang, Guang-Jian Liu, Hang-Tong Hu, Xin Li, Jin-Yu Liang, Zhu Wang, Xiao-Wen Huang, Chu-Qing Zhang, Si-Min Ruan, Xiao-Yan Xie, Ming Kuang, Ming-De Lu, Li-Da Chen, Wei Wang
OBJECTIVE: To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning. MATERIALS AND METHODS: This prospective study consisted of 144 patients with chronic hepatitis B. Ultrasomics-high-throughput quantitative data from ultrasound imaging of liver fibrosis-were generated using conventional radiomics, original radiofrequency (ORF) and contrast-enhanced micro-flow (CEMF) features. Three categories of features were explored using pairwise correlation and hierarchical clustering...
September 3, 2018: European Radiology
Sumit K Banchhor, Narendra D Londhe, Tadashi Araki, Luca Saba, Petia Radeva, Narendra N Khanna, Jasjit S Suri
PURPOSE OF REVIEW: Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease...
October 1, 2018: Computers in Biology and Medicine
Kareem Sallam, Ahmed Refaat, Marwa Romeih
Ultrasound-guided venous access is becoming a standard technique in many centers worldwide. In small veins and in the pediatric population, successful venous puncture is sometimes followed by resistance in passing the wire. The needle seems to miss the small vein during syringe dismounting, wire mounting and wire advancement through the needle. This work describes a "wire-loaded puncture" technique as a solution for this problem. PATIENTS AND METHODS: Paediatric cancer patients who needed venous access for different indications were included in the study...
September 2018: Journal of the Egyptian National Cancer Institute
Esther Puyol, Bram Ruijsink, Bernhard Gerber, Mihaela Silvia Amzulescu, Helene Langet, Mathieu de Craene, Julia A Schnabel, Paolo Piro, Andy P King
OBJECTIVE: The aim of this paper is to describe an automated diagnostic pipeline which uses as input only ultrasound (US) data, but is at the same time informed by a training database of multimodal magnetic resonance (MR) and US image data. METHODS: We create a multimodal cardiac motion atlas from 3D MR and 3D US data followed by multi-view machine learning algorithms to combine and extract the most meaningful cardiac descriptors for classification of dilated cardiomyopathy (DCM) patients using US data only...
August 15, 2018: IEEE Transactions on Bio-medical Engineering
Michał Byra, Grzegorz Styczynski, Cezary Szmigielski, Piotr Kalinowski, Łukasz Michałowski, Rafał Paluszkiewicz, Bogna Ziarkiewicz-Wróblewska, Krzysztof Zieniewicz, Piotr Sobieraj, Andrzej Nowicki
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...
August 9, 2018: International Journal of Computer Assisted Radiology and Surgery
Qiang Zheng, Gregory Tasian, Yong Fan
Classification of ultrasound (US) kidney images for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT) in children is a challenging task. It is desirable to improve existing pattern classification models that are built upon conventional image features. In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children. Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is adopted for transfer learning-based feature extraction from 3-channel feature maps computed from US images, including original images, gradient features, and distanced transform features...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
Ting Xiao, Lei Liu, Kai Li, Wenjian Qin, Shaode Yu, Zhicheng Li
This research aims to address the problem of discriminating benign cysts from malignant masses in breast ultrasound (BUS) images based on Convolutional Neural Networks (CNNs). The biopsy-proven benchmarking dataset was built from 1422 patient cases containing a total of 2058 breast ultrasound masses, comprising 1370 benign and 688 malignant lesions. Three transferred models, InceptionV3, ResNet50, and Xception, a CNN model with three convolutional layers (CNN3), and traditional machine learning-based model with hand-crafted features were developed for differentiating benign and malignant tumors from BUS data...
2018: BioMed Research International
Yuan Xu, Yuxin Wang, Jie Yuan, Qian Cheng, Xueding Wang, Paul L Carson
Breast cancer is the most commonly diagnosed cancer, which alone accounts for 30% all new cancer diagnoses for women, posing a threat to women's health. Segmentation of breast ultrasound images into functional tissues can aid tumor localization, breast density measurement, and assessment of treatment response, which is important to the clinical diagnosis of breast cancer. However, manually segmenting the ultrasound images, which is skill and experience dependent, would lead to a subjective diagnosis; in addition, it is time-consuming for radiologists to review hundreds of clinical images...
January 2019: Ultrasonics
Marie-Helene Roy-Cardinal, Francois Destrempes, Gilles Soulez, Guy Cloutier
Quantitative ultrasound (QUS) imaging methods including elastography, echogenicity analysis and speckle statistical modeling are available from a single ultrasound radiofrequency data acquisition. Since these ultrasound imaging methods provide complementary quantitative tissue information, characterization of carotid artery plaques may gain from their combination. Sixty-six patients with symptomatic (n=26) and asymptomatic (n=40) carotid atherosclerotic plaques were included in the study. Of these, 31 underwent magnetic resonance imaging (MRI) to characterize plaque vulnerability and quantify plaque components...
June 29, 2018: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Wenfeng Song, Shuai Li, Ji Liu, Hong Qin, Bo Zhang, Zhang Shuyang, Aimin Hao
Thyroid ultrasonography is a widely-used clinical technique for nodule diagnosis in thyroid regions. However, it remains difficult to detect and recognize the nodules due to low contrast, high noise, and diverse appearance of nodules. In today's clinical practice, senior doctors could pinpoint nodules by analyzing global context features, local geometry structure, and intensity changes, which would require rich clinical experience accumulated from hundreds and thousands of nodule case studies. To alleviate doctors' tremendous labor in the diagnosis procedure, we advocate a machine learning approach to the detection and recognition tasks in this paper...
July 3, 2018: IEEE Journal of Biomedical and Health Informatics
Mehrdad J Gangeh, Simon Liu, Hadi Tadayyon, Gregory J Czarnota
OBJECTIVE: A computer-assisted technology has recently been proposed for the assessment of therapeutic responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer (LABC). The system, however, extracted features from individual scans in a tumor irrespective of its relation to the other scans of the same patient, ignoring the volumetric information. This study addresses this problem by introducing a novel engineered texton-based method in order to account for volumetric information in the design of textural descriptors to represent tumor scans...
August 2018: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Wioletta Dobkowska-Chudon, Miroslaw Wrobel, Pawel Karlowicz, Andrzej Dabrowski, Andrzej Krupienicz, Tomasz Targowski, Andrzej Nowicki, Robert Olszewski
Acoustocerebrography is a novel, non-invasive, transcranial ultrasonic diagnostic method based on the transmission of multispectral ultrasound signals propagating through the brain tissue. Dedicated signal processing enables the estimation of absorption coefficient, frequency-dependent attenuation, speed of sound and tissue elasticity. Hypertension and atrial fibrillation are well known factors correlated with white matter lesions, intracerebral hemorrhage and cryptogenic stroke numbers. The aim of this study was to compare the acoustocerebrography signal in the brains of asymptomatic atrial fibrillation patients with and without hypertension...
2018: PloS One
Santosh D Bhosale, Robert Moulder, Mikko S Venäläinen, Juhani S Koskinen, Niina Pitkänen, Markus T Juonala, Mika A P Kähönen, Terho J Lehtimäki, Jorma S A Viikari, Laura L Elo, David R Goodlett, Riitta Lahesmaa, Olli T Raitakari
To evaluate the presence of serum protein biomarkers associated with the early phases of formation of carotid atherosclerotic plaques, label-free quantitative proteomics analyses were made for serum samples collected as part of The Cardiovascular Risk in Young Finns Study. Samples from subjects who had an asymptomatic carotid artery plaque detected by ultrasound examination (N = 43, Age = 30-45 years) were compared with plaque free controls (N = 43) (matched for age, sex, body weight and systolic blood pressure)...
June 15, 2018: Scientific Reports
Zahra Hoodbhoy, Babar Hasan, Fyezah Jehan, Bart Bijnens, Devyani Chowdhury
Background: In Pakistan, stillbirth rates and early neonatal mortality rates are amongst the highest in the world. The aim of this study is to provide proof of concept for using a computational model of fetal haemodynamics, combined with machine learning. This model will be based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows with the goal to identify those fetuses at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities...
February 12, 2018: Gates open research
Alberto Boi, Ankush D Jamthikar, Luca Saba, Deep Gupta, Aditya Sharma, Bruno Loi, John R Laird, Narendra N Khanna, Jasjit S Suri
PURPOSE OF REVIEW: Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and severe catastrophic events over time. Identification of these atherosclerotic plaque components is essential to pre-estimate the risk of cardiovascular disease (CVD) and stratify them as a high or low risk. The characterization and quantification of coronary plaque components are not only vital but also a challenging task which can be possible using high-resolution imaging techniques...
May 21, 2018: Current Atherosclerosis Reports
Mainak Biswas, Venkatanareshbabu Kuppili, Tadashi Araki, Damodar Reddy Edla, Elisa Cuadrado Godia, Luca Saba, Harman S Suri, Tomaž Omerzu, John R Laird, Narendra N Khanna, Andrew Nicolaides, Jasjit S Suri
MOTIVATION: The carotid intima-media thickness (cIMT) is an important biomarker for cardiovascular diseases and stroke monitoring. This study presents an intelligence-based, novel, robust, and clinically-strong strategy that uses a combination of deep-learning (DL) and machine-learning (ML) paradigms. METHODOLOGY: A two-stage DL-based system (a class of AtheroEdge™ systems) was proposed for cIMT measurements. Stage I consisted of a convolution layer-based encoder for feature extraction and a fully convolutional network-based decoder for image segmentation...
July 1, 2018: Computers in Biology and Medicine
Makoto Sera
The evaluation of the lung has usually been considered off-limits for ultrasound, because ultrasound energy is rapidly dissipated by air. Lung ultrasound is not useful for the evaluation of the pulmonary parenchyma and the pleural line. However ultrasound machines have become more portable, with decreased start-up time, while simultaneously providing improved image quality and ease of image acquisition. Additionally, lung ultrasound is highly accurate for the diagnosis of pneumothorax, hemothorax. pleural effusions, pulmonary edema (cardiogenic or noncardiogenic), interstitial syndrome, and pneumonia...
May 2017: Masui. the Japanese Journal of Anesthesiology
Qinghua Huang, Fan Zhang, Xuelong Li
The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system...
2018: BioMed Research International
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