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Machine learning and ultrasound

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https://www.readbyqxmd.com/read/30094778/transfer-learning-with-deep-convolutional-neural-network-for-liver-steatosis-assessment-in-ultrasound-images
#1
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
https://www.readbyqxmd.com/read/30079128/transfer-learning-for-diagnosis-of-congenital-abnormalities-of-the-kidney-and-urinary-tract-in-children-based-on-ultrasound-imaging-data
#2
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
https://www.readbyqxmd.com/read/30035122/comparison-of-transferred-deep-neural-networks-in-ultrasonic-breast-masses-discrimination
#3
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
https://www.readbyqxmd.com/read/30029074/medical-breast-ultrasound-image-segmentation-by-machine-learning
#4
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...
July 17, 2018: Ultrasonics
https://www.readbyqxmd.com/read/29994706/assessment-of-carotid-artery-plaque-components-with-machine-learning-classification-using-homodyned-k-parametric-maps-and-elastograms
#5
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
https://www.readbyqxmd.com/read/29994412/multi-task-cascade-convolution-neural-networks-for-automatic-thyroid-nodule-detection-and-recognition
#6
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
https://www.readbyqxmd.com/read/29994306/computer-aided-theragnosis-based-on-tumor-volumetric-information-in-breast-cancer
#7
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
https://www.readbyqxmd.com/read/29979716/detecting-cerebrovascular-changes-in-the-brain-caused-by-hypertension-in-atrial-fibrillation-group-using-acoustocerebrography
#8
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
https://www.readbyqxmd.com/read/29907817/serum-proteomic-profiling-to-identify-biomarkers-of-premature-carotid-atherosclerosis
#9
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
https://www.readbyqxmd.com/read/29863146/machine-learning-from-fetal-flow-waveforms-to-predict-adverse-perinatal-outcomes-a-study-protocol
#10
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
https://www.readbyqxmd.com/read/29781047/a-survey-on-coronary-atherosclerotic-plaque-tissue-characterization-in-intravascular-optical-coherence-tomography
#11
REVIEW
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
https://www.readbyqxmd.com/read/29778925/deep-learning-strategy-for-accurate-carotid-intima-media-thickness-measurement-an-ultrasound-study-on-japanese-diabetic-cohort
#12
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
https://www.readbyqxmd.com/read/29693937/point-of-care-lung-ultrasound
#13
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
https://www.readbyqxmd.com/read/29687000/machine-learning-in-ultrasound-computer-aided-diagnostic-systems-a-survey
#14
REVIEW
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
https://www.readbyqxmd.com/read/29576939/detection-of-linear-features-including-bone-and-skin-areas-in-ultrasound-images-of-joints
#15
Artur Bąk, Jakub Segen, Kamil Wereszczyński, Pawel Mielnik, Marcin Fojcik, Marek Kulbacki
Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images...
2018: PeerJ
https://www.readbyqxmd.com/read/29541649/learning-to-segment-key-clinical-anatomical-structures-in-fetal-neurosonography-informed-by-a-region-based-descriptor
#16
Ruobing Huang, Ana Namburete, Alison Noble
We present a general framework for automatic segmentation of fetal brain structures in ultrasound images inspired by recent advances in machine learning. The approach is based on a region descriptor that characterizes the shape and local intensity context of different neurological structures without explicit models. To validate our framework, we present experiments to segment two fetal brain structures of clinical importance that have quite different ultrasonic appearances-the corpus callosum (CC) and the choroid plexus (CP)...
January 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29498975/the-state-of-technology-in-craniosynostosis
#17
Tyler Safran, Alex Viezel-Mathieu, Benjamin Beland, Alain J Azzi, Rafael Galli, Mirko Gilardino
INTRODUCTION: Craniosynostosis, the premature fusion of ≥1 cranial sutures, is the leading cause of pediatric skull deformities, affecting 1 of every 2000 to 2500 live births worldwide. Technologies used for the management of craniofacial conditions, specifically in craniosynostosis, have been advancing dramatically. This article highlights the most recent technological advances in craniosynostosis surgery through a systematic review of the literature. METHODS: A systematic electronic search was performed using the PubMed database...
June 2018: Journal of Craniofacial Surgery
https://www.readbyqxmd.com/read/29495714/deep-convolutional-neural-networks-for-estimating-porous-material-parameters-with-ultrasound-tomography
#18
Timo Lähivaara, Leo Kärkkäinen, Janne M J Huttunen, Jan S Hesthaven
The feasibility of data based machine learning applied to ultrasound tomography is studied to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, a high-order discontinuous Galerkin method is considered, while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, the material porosity and tortuosity is estimated, while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion...
February 2018: Journal of the Acoustical Society of America
https://www.readbyqxmd.com/read/29492605/machine-learning-for-medical-ultrasound-status-methods-and-future-opportunities
#19
Laura J Brattain, Brian A Telfer, Manish Dhyani, Joseph R Grajo, Anthony E Samir
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices...
April 2018: Abdominal Radiology
https://www.readbyqxmd.com/read/29469937/radiomic-features-on-mri-enable-risk-categorization-of-prostate-cancer-patients-on-active-surveillance-preliminary-findings
#20
Ahmad Algohary, Satish Viswanath, Rakesh Shiradkar, Soumya Ghose, Shivani Pahwa, Daniel Moses, Ivan Jambor, Ronald Shnier, Maret Böhm, Anne-Maree Haynes, Phillip Brenner, Warick Delprado, James Thompson, Marley Pulbrock, Andrei S Purysko, Sadhna Verma, Lee Ponsky, Phillip Stricker, Anant Madabhushi
BACKGROUND: Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS). PURPOSE: To evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients. STUDY TYPE: Retrospective...
February 22, 2018: Journal of Magnetic Resonance Imaging: JMRI
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