Traditional clothing design models based on adaptive meshes cannot reflect.To solve this problem,a clothing simulation design model based on 3D image analysis technology is established.The model uses feature extractio...Traditional clothing design models based on adaptive meshes cannot reflect.To solve this problem,a clothing simulation design model based on 3D image analysis technology is established.The model uses feature extraction and description of image evaluation parameters,and establishes the mapping relationship between image features and simulation results by using the optimal parameter values,thereby obtaining a three-dimensional image simulation analysis environment.On the basis of this model,by obtaining the response results of clothing collision detection and the results of local adaptive processing of clothing meshes,the cutting form and actual cutting effect of clothing are determined to construct a design model.The simulation results show that compared with traditional clothing design models,clothing simulation design based on 3D image analysis technology has a better effect,with the definition of fabric folds increasing by 40%.More striking contrast between light and dark,the resolution increasing by 30%,and clothing details getting a more real manifestation.展开更多
In order to obtain a better sandstone three-dimensional （3D） reconstruction result which is more similar to the original sample, an algorithm based on stationarity for a two-dimensional （2D） training image is prop...In order to obtain a better sandstone three-dimensional （3D） reconstruction result which is more similar to the original sample, an algorithm based on stationarity for a two-dimensional （2D） training image is proposed. The second-order statistics based on texture features are analyzed to evaluate the scale stationarity of the training image. The multiple-point statistics of the training image are applied to obtain the multiple-point statistics stationarity estimation by the multi-point density function. The results show that the reconstructed 3D structures are closer to reality when the training image has better scale stationarity and multiple-point statistics stationarity by the indications of local percolation probability and two-point probability. Moreover, training images with higher multiple-point statistics stationarity and lower scale stationarity are likely to obtain closer results to the real 3D structure, and vice versa. Thus, stationarity analysis of the training image has far-reaching significance in choosing a better 2D thin section image for the 3D reconstruction of porous media. Especially, high-order statistics perform better than low-order statistics.展开更多
A novel technique of three-dimensional (3D) reconstruction, segmentation, display and analysis of series slices of images including microscopic wide field optical sectioning by deconvolution method, cryo-electron micr...A novel technique of three-dimensional (3D) reconstruction, segmentation, display and analysis of series slices of images including microscopic wide field optical sectioning by deconvolution method, cryo-electron microscope slices by Fou-rier-Bessel synthesis and electron tomography (ET), and a series of computed tomography (CT) was developed to perform si-multaneous measurement on the structure and function of biomedical samples. The paper presents the 3D reconstruction seg-mentation display and analysis results of pollen spore, chaperonin, virus, head, cervical bone, tibia and carpus. At the same time, it also puts forward some potential applications of the new technique in the biomedical realm.展开更多
This paper proposes a novel method for analyzing a textile fabric structure to extract positional information regarding each yarn using three-dimensional X-ray computed tomography(3D CT) image.Positional relationship ...This paper proposes a novel method for analyzing a textile fabric structure to extract positional information regarding each yarn using three-dimensional X-ray computed tomography(3D CT) image.Positional relationship among the yarns can be reconstructed using the extracted yarn positional information.In this paper,a sequence of points on the center line of each yarn of the sample is defined as the yarn positional information,since the sequence can be regarded as the representative position of the yarn.The sequence is extracted by tracing the yarn.The yarn is traced by estimating the yarn center and direction and correlating the yarn part of the 3D CT image with a 3D yarn model,which is moved along the estimated yarn direction.The trajectory of the center of the yarn model corresponds to the positional information of the yarn.The application of the proposed method is shown by experimentally applying the proposed method to a 3D CT image of a double-layered woven fabric.Furthermore,the experimental results for a plain knitted fabric show that this method can be applied to even knitted fabrics.展开更多
The three-dimensional spectral analysis method was applied to airglow data from September 2023 to August 2024 derivedfrom an OH airglow imager located at the Hejing station (42.79°N, 83.73°E) to study the pr...The three-dimensional spectral analysis method was applied to airglow data from September 2023 to August 2024 derivedfrom an OH airglow imager located at the Hejing station (42.79°N, 83.73°E) to study the propagation characteristics of gravity waves(GWs) over Northwest China. We found that obvious seasonal variations occur in the propagation of GWs. In spring, GWs mainlypropagate in the northeast direction. In summer and autumn, GWs mainly propagate in the north direction. However, GWs mainlypropagate in the south direction in winter. The direction of GW propagation in the zonal direction is controlled by the wind-filteringeffect, whereas the north–south meridional direction is mainly determined by the location of the wave source. We found that the averageenergy spectrum exhibits a 10%–20% higher intensity in summer and winter compared with spring and autumn. For the first time, wereport the seasonal variation characteristics of GWs over the inland areas of Northwest China, which is of great significance forunderstanding the regional distribution characteristics of GWs.展开更多
Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
BACKGROUND Congestive hepatopathy,also known as nutmeg liver,is liver damage secondary to chronic heart failure(HF).Its morphological characteristics in terms of medical imaging are not defined and remain unclear.AIM ...BACKGROUND Congestive hepatopathy,also known as nutmeg liver,is liver damage secondary to chronic heart failure(HF).Its morphological characteristics in terms of medical imaging are not defined and remain unclear.AIM To leverage machine learning to capture imaging features of congestive hepatopathy using incidentally acquired computed tomography(CT)scans.METHODS We retrospectively analyzed 179 chronic HF patients who underwent echocardiography and CT within one year.Right HF severity was classified into three grades.Liver CT images at the paraumbilical vein level were used to develop a ResNet-based machine learning model to predict tricuspid regurgitation(TR)severity.Model accuracy was compared with that of six gastroenterology and four radiology experts.RESULTS In the included patients,120 were male(mean age:73.1±14.4 years).The accuracy of the results predicting TR severity from a single CT image for the machine learning model was significantly higher than the average accuracy of the experts.The model was found to be exceptionally reliable for predicting severe TR.CONCLUSION Deep learning models,particularly those using ResNet architectures,can help identify morphological changes associated with TR severity,aiding in early liver dysfunction detection in patients with HF,thereby improving outcomes.展开更多
Corporate image is the external manifestation of a company’s cultural and spiritual essence,as well as the overall impression formed through its interactions with the public.Huawei,as a successful multinational enter...Corporate image is the external manifestation of a company’s cultural and spiritual essence,as well as the overall impression formed through its interactions with the public.Huawei,as a successful multinational enterprise,has established a robust corporate image in the international market through technological innovation and brand building.Moreover,Huawei’s development is closely aligned with national policies and strategies,making it a representative enterprise for showcasing China’s technological independence and national image.This study examines Huawei’s English press releases on product launches published between 2022 and 2024 and conducts a comparative analysis with similar materials from Apple’s official website.Based on Fairclough’s three-dimensional discourse analysis model,this research explores the linguistic features of Huawei’s corporate image construction from the perspectives of text,discourse practice,and social practice.The findings reveal that Huawei has successfully constructed a corporate image that emphasizes technological innovation,prioritizes user needs,and underscores its identity as a national enterprise.This study not only sheds light on Huawei’s strategies for image construction in international competition but also provides a valuable reference for Chinese enterprises in their cultural communication and brand building during the globalization process.展开更多
Applying visual grammar theory,this study examines representational,interactive,and compositional meanings of the giant panda in Western media cartoons related to China from 1999 to the present.Distinct phases in the ...Applying visual grammar theory,this study examines representational,interactive,and compositional meanings of the giant panda in Western media cartoons related to China from 1999 to the present.Distinct phases in the panda’s representation were identified and illustrated by cases of cartoons in major Western media.These phases trace shift of panda cartoon image from a symbol of peace and friendliness to a politicized emblem of China’s international stance.Key visual trends,such as transitivity,color symbolism,scale enlargement,and increasing compositional complexity,embody the panda’s role in shaping China’s global image and its function in international discourse.These trends reflect the panda’s transformation into a contested symbol,which mediates between China’s self-representation and Western perceptions of its geopolitical rise.By situating the analysis within the context of China’s growing global influence,this study contributes to visual and media studies,demonstrating how cultural symbols are recontextualized to reflect and shape geopolitical narratives.展开更多
Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have m...Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.展开更多
The recently introduced real-time three-dimensional color Doppler flow imaging （RT-3D CDFI） technique provides a quick and accurate calculation of regurgitant jet volume （RJV） and fraction. In order to evaluate RT...The recently introduced real-time three-dimensional color Doppler flow imaging （RT-3D CDFI） technique provides a quick and accurate calculation of regurgitant jet volume （RJV） and fraction. In order to evaluate RT-3D CDFI in the noninvasive assessment of aortic RJV and regurgitant jet fraction （RJF） in patients with isolated aortic regurgitation, real-time three-dimensional echocardiographic studies were performed on 23 patients with isolated aortic regurgitation to obtain LV end-diastolic volumes （LVEDV）, end-systolic volumes （LVESV） and RJV, and then RJF could be calculated. The regurgitant volume （RV） and regurgitant fraction （RF） calculated by two-dimensional pulsed Doppler （2D-PD） method served as reference values. The results showed that aortic RJV measured by the RT-3D CDFI method showed a good correlation with the 2D-PD measurements （r= 0.93, Y=0.89X＋ 3.9, SEE= 8.6 mL, P〈0.001 ）; the mean （SD） difference between the two methods was - 1.5 （9.8） mL. % RJF estimated by the RT-3D CDFI method was also correlated well with the values obtained by the 2D-PD method （r=0.88, Y=0.71X＋ 14.8, SEE= 6.4 %, P〈0. 001）; the mean （SD） difference between the two methods was -1.2 （7.9） %. It was suggested that the newly developed RT-3D CDFI technique was feasible in the majority of patients. In patients with eccentric aortic regurgitation, this new modality provides additional information to that obtained from the two-dimensional examination, which overcomes the inherent limitations of two-dimensional echocardiography by depicting the full extent of the jet trajectory. In addition, the RT-3D CDFI method is quick and accurate in calculating RJV and RJF.展开更多
Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional imag...Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.展开更多
To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation f...To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.展开更多
Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination...Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.展开更多
BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features ...BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.展开更多
BACKGROUND Hepatobiliary surgery is complex and requires a thorough understanding of the liver’s anatomy,biliary system,and vasculature.Traditional imaging methods such as computed tomography(CT)and magnetic resonanc...BACKGROUND Hepatobiliary surgery is complex and requires a thorough understanding of the liver’s anatomy,biliary system,and vasculature.Traditional imaging methods such as computed tomography(CT)and magnetic resonance imaging(MRI),although helpful,fail to provide three-dimensional(3D)relationships of these structures,which are critical for planning and executing complicated surgeries.AIM To explore the use of 3D imaging and virtual surgical planning(VSP)technologies to improve surgical accuracy,reduce complications,and enhance patient recovery in hepatobiliary surgeries.METHODS A comprehensive review of studies published between 2017 and 2024 was conducted through PubMed,Scopus,Google Scholar,and Web of Science.Studies selected focused on 3D imaging and VSP applications in hepatobiliary surgery,assessing surgical precision,complications,and patient outcomes.Thirty studies,including randomized controlled trials,cohort studies,and case reports,were included in the final analysis.RESULTS Various 3D imaging modalities,including multidetector CT,MRI,and 3D rotational angiography,provide high-resolution views of the liver’s vascular and biliary anatomy.VSP allows surgeons to simulate complex surgeries,improving preoperative planning and reducing complications like bleeding and bile leaks.Several studies have demonstrated improved surgical precision,reduced complications,and faster recovery times when 3D imaging and VSP were used in complex surgeries.CONCLUSION 3D imaging and VSP technologies significantly enhance the accuracy and outcomes of hepatobiliary surgeries by providing individualized preoperative planning.While promising,further research,particularly randomized controlled trials,is needed to standardize protocols and evaluate long-term efficacy.展开更多
The estimation of shear strength of rock mass discontinuity is always a focal, but difficult, problem in the field of geotechnical engineering. Considering the disadvantages and limitation of exist- ing estimation met...The estimation of shear strength of rock mass discontinuity is always a focal, but difficult, problem in the field of geotechnical engineering. Considering the disadvantages and limitation of exist- ing estimation methods, a new approach based on the shadow area percentage （SAP） that can be used to quantify surface roughness is proposed in this article. Firstly, by the help of laser scanning technique, the three-dimensional model of the surface of rock discontinuity was established. Secondly, a light source was simulated, and there would be some shadows produced on the model surface. Thirdly, to obtain the value of SAP of each specimen, the shadow detection technique was introduced for use. Fourthly, compared with the result from direct shear testing and based on statistics, an empirical for- mula was found among SAP, normal stress, and shear strength. Data of Yujian （~ River were used as an example, and the following conclusions have been made. （1） In the case of equal normal stress, the peak shear stress is positively proportional to the SAP. （2） The formula for estimating was derived, and the predictions of peak-shear strength made with this equation well agreed with the experimental re- suits obtained in laboratory tests.展开更多
BACKGROUND It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning.It has not been extensively investigated whether texture features derived from diffusion-weighted...BACKGROUND It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning.It has not been extensively investigated whether texture features derived from diffusion-weighted imaging(DWI)images and apparent diffusion coefficient(ADC)maps are associated with the extent of local invasion(pathological stage T1-2 vs T3-4)and nodal involvement(pathological stage N0 vs N1-2)in rectal cancer.AIM To predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.METHODS One hundred and fifteen patients with pathologically proven rectal cancer,who underwent preoperative magnetic resonance imaging,including DWI,were enrolled,retrospectively.The ADC measurements(ADCmean,ADCmin,ADCmax)as well as texture features,including the gray level co-occurrence matrix parameters,the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI(b=0 and b=1000)images and the ADC maps.Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis.Multivariate logistic regression analysis was conducted to establish the models.The predictive performance was validated by receiver operating characteristic curve analysis.RESULTS Dissimilarity,sum average,information correlation and run-length nonuniformity from DWIb=0 images,gray level nonuniformity,run percentage and run-length nonuniformity from DWIb=1000 images,and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion(stage T3-4).The area under the operating characteristic curve of the model reached 0.793 with a sensitivity of 78.57%and a specificity of 74.19%.Sum average,gray level nonuniformity and the horizontal components of symlet transform(SymletH)from DWIb=0 images,sum average,information correlation,long run low gray level emphasis and SymletH from DWIb=1000 images,and ADCmax,ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement.The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77%and a specificity of 68.25%.CONCLUSION Texture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.展开更多
Confocal laser endomicroscopy(CLE)has become an indispensable tool in the diagnosis and detection of gastrointestinal(GI)diseases due to its high-resolution and high-contrast imaging capabilities.However,the early-sta...Confocal laser endomicroscopy(CLE)has become an indispensable tool in the diagnosis and detection of gastrointestinal(GI)diseases due to its high-resolution and high-contrast imaging capabilities.However,the early-stage imaging changes of gastrointestinal disorders are often subtle,and traditional medical image analysis methods rely heavily on manual interpretation,which is time-consuming,subject to observer variability,and inefficient for accurate lesion identification across large-scale image datasets.With the introduction of artificial intelligence(AI)technologies,AI-driven CLE image analysis systems can automatically extract pathological features and have demonstrated significant clinical value in lesion recognition,classification diagnosis,and malignancy prediction of GI diseases.These systems greatly enhance diagnostic efficiency and early detection capabilities.This review summarizes the applications of AI-assisted CLE in GI diseases,analyzes the limitations of current technologies,and explores future research directions.It is expected that the deep integration of AI and confocal imaging technologies will provide strong support for precision diagnosis and personalized treatment in the field of gastrointestinal disorders.展开更多
文摘Traditional clothing design models based on adaptive meshes cannot reflect.To solve this problem,a clothing simulation design model based on 3D image analysis technology is established.The model uses feature extraction and description of image evaluation parameters,and establishes the mapping relationship between image features and simulation results by using the optimal parameter values,thereby obtaining a three-dimensional image simulation analysis environment.On the basis of this model,by obtaining the response results of clothing collision detection and the results of local adaptive processing of clothing meshes,the cutting form and actual cutting effect of clothing are determined to construct a design model.The simulation results show that compared with traditional clothing design models,clothing simulation design based on 3D image analysis technology has a better effect,with the definition of fabric folds increasing by 40%.More striking contrast between light and dark,the resolution increasing by 30%,and clothing details getting a more real manifestation.
基金The National Natural Science Foundation of China(No.60972130)
文摘In order to obtain a better sandstone three-dimensional （3D） reconstruction result which is more similar to the original sample, an algorithm based on stationarity for a two-dimensional （2D） training image is proposed. The second-order statistics based on texture features are analyzed to evaluate the scale stationarity of the training image. The multiple-point statistics of the training image are applied to obtain the multiple-point statistics stationarity estimation by the multi-point density function. The results show that the reconstructed 3D structures are closer to reality when the training image has better scale stationarity and multiple-point statistics stationarity by the indications of local percolation probability and two-point probability. Moreover, training images with higher multiple-point statistics stationarity and lower scale stationarity are likely to obtain closer results to the real 3D structure, and vice versa. Thus, stationarity analysis of the training image has far-reaching significance in choosing a better 2D thin section image for the 3D reconstruction of porous media. Especially, high-order statistics perform better than low-order statistics.
文摘A novel technique of three-dimensional (3D) reconstruction, segmentation, display and analysis of series slices of images including microscopic wide field optical sectioning by deconvolution method, cryo-electron microscope slices by Fou-rier-Bessel synthesis and electron tomography (ET), and a series of computed tomography (CT) was developed to perform si-multaneous measurement on the structure and function of biomedical samples. The paper presents the 3D reconstruction seg-mentation display and analysis results of pollen spore, chaperonin, virus, head, cervical bone, tibia and carpus. At the same time, it also puts forward some potential applications of the new technique in the biomedical realm.
基金Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science(2006-No.18800064)
文摘This paper proposes a novel method for analyzing a textile fabric structure to extract positional information regarding each yarn using three-dimensional X-ray computed tomography(3D CT) image.Positional relationship among the yarns can be reconstructed using the extracted yarn positional information.In this paper,a sequence of points on the center line of each yarn of the sample is defined as the yarn positional information,since the sequence can be regarded as the representative position of the yarn.The sequence is extracted by tracing the yarn.The yarn is traced by estimating the yarn center and direction and correlating the yarn part of the 3D CT image with a 3D yarn model,which is moved along the estimated yarn direction.The trajectory of the center of the yarn model corresponds to the positional information of the yarn.The application of the proposed method is shown by experimentally applying the proposed method to a 3D CT image of a double-layered woven fabric.Furthermore,the experimental results for a plain knitted fabric show that this method can be applied to even knitted fabrics.
基金supported by the National Science Foundation of China(Grant Nos.42374205 and 41974179)the Specialized Research Fund of the National Space Science Center,Chinese Academy of Sciences(Grant No.E4PD3010)supported by the Specialized Research Fund for State Key Laboratories.
文摘The three-dimensional spectral analysis method was applied to airglow data from September 2023 to August 2024 derivedfrom an OH airglow imager located at the Hejing station (42.79°N, 83.73°E) to study the propagation characteristics of gravity waves(GWs) over Northwest China. We found that obvious seasonal variations occur in the propagation of GWs. In spring, GWs mainlypropagate in the northeast direction. In summer and autumn, GWs mainly propagate in the north direction. However, GWs mainlypropagate in the south direction in winter. The direction of GW propagation in the zonal direction is controlled by the wind-filteringeffect, whereas the north–south meridional direction is mainly determined by the location of the wave source. We found that the averageenergy spectrum exhibits a 10%–20% higher intensity in summer and winter compared with spring and autumn. For the first time, wereport the seasonal variation characteristics of GWs over the inland areas of Northwest China, which is of great significance forunderstanding the regional distribution characteristics of GWs.
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金Supported by Grant-in-Aid for Research on Hepatitis from the Japan Agency for Medical Research and Development,No.24fk0210128h0002Grant-in-Aid for Scientific Research,No.KAKENHI-23K07372.
文摘BACKGROUND Congestive hepatopathy,also known as nutmeg liver,is liver damage secondary to chronic heart failure(HF).Its morphological characteristics in terms of medical imaging are not defined and remain unclear.AIM To leverage machine learning to capture imaging features of congestive hepatopathy using incidentally acquired computed tomography(CT)scans.METHODS We retrospectively analyzed 179 chronic HF patients who underwent echocardiography and CT within one year.Right HF severity was classified into three grades.Liver CT images at the paraumbilical vein level were used to develop a ResNet-based machine learning model to predict tricuspid regurgitation(TR)severity.Model accuracy was compared with that of six gastroenterology and four radiology experts.RESULTS In the included patients,120 were male(mean age:73.1±14.4 years).The accuracy of the results predicting TR severity from a single CT image for the machine learning model was significantly higher than the average accuracy of the experts.The model was found to be exceptionally reliable for predicting severe TR.CONCLUSION Deep learning models,particularly those using ResNet architectures,can help identify morphological changes associated with TR severity,aiding in early liver dysfunction detection in patients with HF,thereby improving outcomes.
文摘Corporate image is the external manifestation of a company’s cultural and spiritual essence,as well as the overall impression formed through its interactions with the public.Huawei,as a successful multinational enterprise,has established a robust corporate image in the international market through technological innovation and brand building.Moreover,Huawei’s development is closely aligned with national policies and strategies,making it a representative enterprise for showcasing China’s technological independence and national image.This study examines Huawei’s English press releases on product launches published between 2022 and 2024 and conducts a comparative analysis with similar materials from Apple’s official website.Based on Fairclough’s three-dimensional discourse analysis model,this research explores the linguistic features of Huawei’s corporate image construction from the perspectives of text,discourse practice,and social practice.The findings reveal that Huawei has successfully constructed a corporate image that emphasizes technological innovation,prioritizes user needs,and underscores its identity as a national enterprise.This study not only sheds light on Huawei’s strategies for image construction in international competition but also provides a valuable reference for Chinese enterprises in their cultural communication and brand building during the globalization process.
基金supported by the Wuhan University Undergraduate Project of Innovation and Entrepreneurship Training“The Evolution of Cartoon Images of Pandas in Western Media’s China-Related News From the Perspective of Multimodal Theory”(Project Number:S202410486013).
文摘Applying visual grammar theory,this study examines representational,interactive,and compositional meanings of the giant panda in Western media cartoons related to China from 1999 to the present.Distinct phases in the panda’s representation were identified and illustrated by cases of cartoons in major Western media.These phases trace shift of panda cartoon image from a symbol of peace and friendliness to a politicized emblem of China’s international stance.Key visual trends,such as transitivity,color symbolism,scale enlargement,and increasing compositional complexity,embody the panda’s role in shaping China’s global image and its function in international discourse.These trends reflect the panda’s transformation into a contested symbol,which mediates between China’s self-representation and Western perceptions of its geopolitical rise.By situating the analysis within the context of China’s growing global influence,this study contributes to visual and media studies,demonstrating how cultural symbols are recontextualized to reflect and shape geopolitical narratives.
文摘Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.
文摘The recently introduced real-time three-dimensional color Doppler flow imaging （RT-3D CDFI） technique provides a quick and accurate calculation of regurgitant jet volume （RJV） and fraction. In order to evaluate RT-3D CDFI in the noninvasive assessment of aortic RJV and regurgitant jet fraction （RJF） in patients with isolated aortic regurgitation, real-time three-dimensional echocardiographic studies were performed on 23 patients with isolated aortic regurgitation to obtain LV end-diastolic volumes （LVEDV）, end-systolic volumes （LVESV） and RJV, and then RJF could be calculated. The regurgitant volume （RV） and regurgitant fraction （RF） calculated by two-dimensional pulsed Doppler （2D-PD） method served as reference values. The results showed that aortic RJV measured by the RT-3D CDFI method showed a good correlation with the 2D-PD measurements （r= 0.93, Y=0.89X＋ 3.9, SEE= 8.6 mL, P〈0.001 ）; the mean （SD） difference between the two methods was - 1.5 （9.8） mL. % RJF estimated by the RT-3D CDFI method was also correlated well with the values obtained by the 2D-PD method （r=0.88, Y=0.71X＋ 14.8, SEE= 6.4 %, P〈0. 001）; the mean （SD） difference between the two methods was -1.2 （7.9） %. It was suggested that the newly developed RT-3D CDFI technique was feasible in the majority of patients. In patients with eccentric aortic regurgitation, this new modality provides additional information to that obtained from the two-dimensional examination, which overcomes the inherent limitations of two-dimensional echocardiography by depicting the full extent of the jet trajectory. In addition, the RT-3D CDFI method is quick and accurate in calculating RJV and RJF.
基金Projects(50934002,51074013,51304076,51104100)supported by the National Natural Science Foundation of ChinaProject(IRT0950)supported by the Program for Changjiang Scholars Innovative Research Team in Universities,ChinaProject(2012M510007)supported by China Postdoctoral Science Foundation
文摘Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.
文摘To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.
基金supports by the National Natural Science Foundation of China(Nos.82201135)"2015"Cultivation Program for Reserve Talents for Academic Leaders of Nanjing Stomatological School,Medical School of Nanjing University(No.0223A204).
文摘Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.
文摘BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.
文摘BACKGROUND Hepatobiliary surgery is complex and requires a thorough understanding of the liver’s anatomy,biliary system,and vasculature.Traditional imaging methods such as computed tomography(CT)and magnetic resonance imaging(MRI),although helpful,fail to provide three-dimensional(3D)relationships of these structures,which are critical for planning and executing complicated surgeries.AIM To explore the use of 3D imaging and virtual surgical planning(VSP)technologies to improve surgical accuracy,reduce complications,and enhance patient recovery in hepatobiliary surgeries.METHODS A comprehensive review of studies published between 2017 and 2024 was conducted through PubMed,Scopus,Google Scholar,and Web of Science.Studies selected focused on 3D imaging and VSP applications in hepatobiliary surgery,assessing surgical precision,complications,and patient outcomes.Thirty studies,including randomized controlled trials,cohort studies,and case reports,were included in the final analysis.RESULTS Various 3D imaging modalities,including multidetector CT,MRI,and 3D rotational angiography,provide high-resolution views of the liver’s vascular and biliary anatomy.VSP allows surgeons to simulate complex surgeries,improving preoperative planning and reducing complications like bleeding and bile leaks.Several studies have demonstrated improved surgical precision,reduced complications,and faster recovery times when 3D imaging and VSP were used in complex surgeries.CONCLUSION 3D imaging and VSP technologies significantly enhance the accuracy and outcomes of hepatobiliary surgeries by providing individualized preoperative planning.While promising,further research,particularly randomized controlled trials,is needed to standardize protocols and evaluate long-term efficacy.
基金supported by the China Geological Survey (No.1212011014030)the Major State Basic Research Development Program of China (973 Program) (No.2011CB710600)
文摘The estimation of shear strength of rock mass discontinuity is always a focal, but difficult, problem in the field of geotechnical engineering. Considering the disadvantages and limitation of exist- ing estimation methods, a new approach based on the shadow area percentage （SAP） that can be used to quantify surface roughness is proposed in this article. Firstly, by the help of laser scanning technique, the three-dimensional model of the surface of rock discontinuity was established. Secondly, a light source was simulated, and there would be some shadows produced on the model surface. Thirdly, to obtain the value of SAP of each specimen, the shadow detection technique was introduced for use. Fourthly, compared with the result from direct shear testing and based on statistics, an empirical for- mula was found among SAP, normal stress, and shear strength. Data of Yujian （~ River were used as an example, and the following conclusions have been made. （1） In the case of equal normal stress, the peak shear stress is positively proportional to the SAP. （2） The formula for estimating was derived, and the predictions of peak-shear strength made with this equation well agreed with the experimental re- suits obtained in laboratory tests.
基金Supported by Research and Development Foundation for Major Science and Technology from Shenyang,No.19-112-4-105Big Data Foundation for Health Care from China Medical University,No.HMB201902105Natural Fund Guidance Plan from Liaoning,No.2019-ZD-0743.
文摘BACKGROUND It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning.It has not been extensively investigated whether texture features derived from diffusion-weighted imaging(DWI)images and apparent diffusion coefficient(ADC)maps are associated with the extent of local invasion(pathological stage T1-2 vs T3-4)and nodal involvement(pathological stage N0 vs N1-2)in rectal cancer.AIM To predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.METHODS One hundred and fifteen patients with pathologically proven rectal cancer,who underwent preoperative magnetic resonance imaging,including DWI,were enrolled,retrospectively.The ADC measurements(ADCmean,ADCmin,ADCmax)as well as texture features,including the gray level co-occurrence matrix parameters,the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI(b=0 and b=1000)images and the ADC maps.Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis.Multivariate logistic regression analysis was conducted to establish the models.The predictive performance was validated by receiver operating characteristic curve analysis.RESULTS Dissimilarity,sum average,information correlation and run-length nonuniformity from DWIb=0 images,gray level nonuniformity,run percentage and run-length nonuniformity from DWIb=1000 images,and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion(stage T3-4).The area under the operating characteristic curve of the model reached 0.793 with a sensitivity of 78.57%and a specificity of 74.19%.Sum average,gray level nonuniformity and the horizontal components of symlet transform(SymletH)from DWIb=0 images,sum average,information correlation,long run low gray level emphasis and SymletH from DWIb=1000 images,and ADCmax,ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement.The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77%and a specificity of 68.25%.CONCLUSION Texture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.
基金Supported by Interdisciplinary Program of Shanghai Jiao Tong University,No.YG2024 LC01National Natural Science Foundation of China,No.62406190.
文摘Confocal laser endomicroscopy(CLE)has become an indispensable tool in the diagnosis and detection of gastrointestinal(GI)diseases due to its high-resolution and high-contrast imaging capabilities.However,the early-stage imaging changes of gastrointestinal disorders are often subtle,and traditional medical image analysis methods rely heavily on manual interpretation,which is time-consuming,subject to observer variability,and inefficient for accurate lesion identification across large-scale image datasets.With the introduction of artificial intelligence(AI)technologies,AI-driven CLE image analysis systems can automatically extract pathological features and have demonstrated significant clinical value in lesion recognition,classification diagnosis,and malignancy prediction of GI diseases.These systems greatly enhance diagnostic efficiency and early detection capabilities.This review summarizes the applications of AI-assisted CLE in GI diseases,analyzes the limitations of current technologies,and explores future research directions.It is expected that the deep integration of AI and confocal imaging technologies will provide strong support for precision diagnosis and personalized treatment in the field of gastrointestinal disorders.
