This paper deals with the temperature correlation of gray scale of B-mode ultrasound image from heated tissue. In this study, many in-vitro fresh pig livers are heated in a temperature range from 28 ℃ to 45℃, from w...This paper deals with the temperature correlation of gray scale of B-mode ultrasound image from heated tissue. In this study, many in-vitro fresh pig livers are heated in a temperature range from 28 ℃ to 45℃, from which a series of B-mode ultrasonic images of livers were obtained. The gray-value is evaluated from the ultrasound images respectively. A correlation of the mean gray value of the selected regions (12×12 pixels) in B-mode ultrasonic images of liver and its temperature was pointed out. And the experiment results agreed the evaluation well. And it is possible to monitor the tissue temperature changing in hyperthermia using this correlation.展开更多
Objective To study the role of bladder trabeculation found by B-mode ultrasound in evaluating the degree of bladder outlet obstruction ( BOO ) and the bladder function in benign prostatic hyperplasia ( BPH) patients. ...Objective To study the role of bladder trabeculation found by B-mode ultrasound in evaluating the degree of bladder outlet obstruction ( BOO ) and the bladder function in benign prostatic hyperplasia ( BPH) patients. Methods Conducted prospective research to determine differences in clinical data and urodynamic展开更多
Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high tempora...Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.展开更多
Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives train...Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence,it is expensive due to a metaheuristic search of features occupying the global region of interest(ROI)structures of input images.Thus,it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pretrained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhan-cing the pretrained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths(18,50,and 101)from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results,it has been observed that the combined local and global ROI structure of small depth residual network ResNet18(0.8 in%)has produced significant improve-ment in pixel ratio as compared to ResNet50(0.5 in%)and ResNet101(0.3 in%),respectively.Subsequently,the pretrained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors(Benign and Malignant)and improve the diagnostic accuracy(86%)compared to Dense Net,Alex Net,VGG Net,and Google Net.Moreover,it reduces the computational complexity due to the small depth residual network ResNet18,respectively.展开更多
基金The research was supported by National Nature Science Foundation (30470450) Education Committee Foundation( KP0608200201 ) Elitist Foundation( KW5800200351 ) from Beijing City,China.
文摘This paper deals with the temperature correlation of gray scale of B-mode ultrasound image from heated tissue. In this study, many in-vitro fresh pig livers are heated in a temperature range from 28 ℃ to 45℃, from which a series of B-mode ultrasonic images of livers were obtained. The gray-value is evaluated from the ultrasound images respectively. A correlation of the mean gray value of the selected regions (12×12 pixels) in B-mode ultrasonic images of liver and its temperature was pointed out. And the experiment results agreed the evaluation well. And it is possible to monitor the tissue temperature changing in hyperthermia using this correlation.
文摘Objective To study the role of bladder trabeculation found by B-mode ultrasound in evaluating the degree of bladder outlet obstruction ( BOO ) and the bladder function in benign prostatic hyperplasia ( BPH) patients. Methods Conducted prospective research to determine differences in clinical data and urodynamic
基金the National Natural Science Foundation of China,Nos.92159305,92259303,62027901,81930053,and 82272029Beijing Science Fund for Distinguished Young Scholars,No.JQ22013and Excellent Member Project of the Youth Innovation Promotion Association CAS,No.2016124.
文摘Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.
文摘Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence,it is expensive due to a metaheuristic search of features occupying the global region of interest(ROI)structures of input images.Thus,it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pretrained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhan-cing the pretrained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths(18,50,and 101)from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results,it has been observed that the combined local and global ROI structure of small depth residual network ResNet18(0.8 in%)has produced significant improve-ment in pixel ratio as compared to ResNet50(0.5 in%)and ResNet101(0.3 in%),respectively.Subsequently,the pretrained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors(Benign and Malignant)and improve the diagnostic accuracy(86%)compared to Dense Net,Alex Net,VGG Net,and Google Net.Moreover,it reduces the computational complexity due to the small depth residual network ResNet18,respectively.
