Nonlinear analysis of heart rate variability （HRV） has become important as heart behaves as a complex system. In this work, the approximate entropy （ApEn） has been used as a nonlinear measure. A new concept of est...Nonlinear analysis of heart rate variability （HRV） has become important as heart behaves as a complex system. In this work, the approximate entropy （ApEn） has been used as a nonlinear measure. A new concept of estimating the ApEn in different segments of long length of the recorded data called modified multiple scale （segment） entropy （MMPE） is introduced. The idea of estimating the approximate entropy in different segments is useful to detect the nonlinear dynamics of the heart present in the entire length of data. The present work has been carried out for three cases namely the normal healthy heart （NHH） data, congestive heart failure （CHF） data and Atrial fibrillation （AF） data and the data are analyzed using MMPE techniques. It is observed that the mean value of ApEn for NHH data is much higher than the mean values for CHF data and AF data. The ApEn profiles of CHF, AF and NHH data for different segments obtained using MPE profiles measures the heart dynamism for the three different cases. Also the power spectral density is obtained using fast fourier transform （FFT） analysis and the ratio of LF/HF （low frequency/high frequency） power are computed on multiple scales/segments namely MPLH （multiple scale low frequency to high frequency） for the NHH data, CHF data and AF data and analyzed using MPLH techniques. The results are presented and discussed in the paper.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made re...Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.展开更多
Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progr...Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.展开更多
Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of...Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.展开更多
Background:Diabetic retinopathy(DR)is one of the primary causes of visual impairment globally,resulting from microvascular abnormalities in the retina.Accurate segmentation of retinal blood vessels from fundus images ...Background:Diabetic retinopathy(DR)is one of the primary causes of visual impairment globally,resulting from microvascular abnormalities in the retina.Accurate segmentation of retinal blood vessels from fundus images plays a pivotal role in the early diagnosis,progression monitoring,and treatment planning of DR and related ocular conditions.Traditional convolutional neural networks often struggle with capturing the intricate structures of thin vessels under varied illumination and contrast conditions.Methods:In this study,we propose an improved U-Net-based framework named MSAC U-Net,which enhances feature extraction and reconstruction through multiscale and attention-based modules.Specifically,the encoder replaces standard convolutions with a Multiscale Asymmetric Convolution(MSAC)block,incorporating parallel 1×n,n×1,and n×n kernels at different scales(3×3,5×5,7×7)to effectively capture fine-grained vascular structures.To further refine spatial representation,skip connections are utilized,and the decoder is augmented with dual activation strategies,Squeeze-and-Excitation blocks,and Convolutional Block Attention Modules for improved contextual understanding.Results:The model was evaluated on the publicly available DRIVE dataset.It achieved an accuracy of 96.48%,sensitivity of 88.31%,specificity of 97.90%,and an AUC of 98.59%,demonstrating superior performance compared to several state-of-the-art segmentation methods.Conclusion:The proposed MSAC U-Net provides a robust and accurate approach for retinal vessel segmentation,offering substantial clinical value in the early detection and management of diabetic retinopathy.Its design contributes to enhanced segmentation reliability and may serve as a foundation for broader applications in medical image analysis.展开更多
Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ...Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.展开更多
Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion siz...Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes.To overcome these limitations,we introduce MSAMamba-UNet,a lightweight model that integrates two novel architectures:Multi-Scale Mamba(MSMamba)and Adaptive Dynamic Gating Block(ADGB).MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets.ADGB dynamically selects convolutional kernels with varying receptive fields based on input features,improving the model’s capacity to accommodate diverse lesion textures and scales.Additionally,we introduce a Mix Attention Fusion Block(MAF)to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms.Extensive evaluation of MSAMamba-UNet on the ISIC 2016,ISIC 2017,and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs.Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%,85.47%,and 82.22%,as well as DSC scores of 92.20%,92.17%,and 90.24%,respectively.These results underscore the lightweight design and effectiveness of MSAMamba-UNet.展开更多
3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
Semantic segmentation has made significant breakthroughs in various application fields,but achieving both accurate and efficient segmentation with limited computational resources remains a major challenge.To this end,...Semantic segmentation has made significant breakthroughs in various application fields,but achieving both accurate and efficient segmentation with limited computational resources remains a major challenge.To this end,we propose CGMISeg,an efficient semantic segmentation architecture based on a context-guided multi-scale interaction strategy,aiming to significantly reduce computational overhead while maintaining segmentation accuracy.CGMISeg consists of three core components:context-aware attention modulation,feature reconstruction,and crossinformation fusion.Context-aware attention modulation is carefully designed to capture key contextual information through channel and spatial attention mechanisms.The feature reconstruction module reconstructs contextual information from different scales,modeling key rectangular areas by capturing critical contextual information in both horizontal and vertical directions,thereby enhancing the focus on foreground features.The cross-information fusion module aims to fuse the reconstructed high-level features with the original low-level features during upsampling,promoting multi-scale interaction and enhancing the model’s ability to handle objects at different scales.We extensively evaluated CGMISeg on ADE20K,Cityscapes,and COCO-Stuff,three widely used datasets benchmarks,and the experimental results show that CGMISeg exhibits significant advantages in segmentation performance,computational efficiency,and inference speed,clearly outperforming several mainstream methods,including SegFormer,Feedformer,and SegNext.Specifically,CGMISeg achieves 42.9%mIoU(Mean Intersection over Union)and 15.7 FPS(Frames Per Second)on the ADE20K dataset with 3.8 GFLOPs(Giga Floating-point Operations Per Second),outperforming Feedformer and SegNeXt by 3.7%and 1.8%in mIoU,respectively,while also offering reduced computational complexity and faster inference.CGMISeg strikes an excellent balance between accuracy and efficiency,significantly enhancing both computational and inference performance while maintaining high precision,showcasing exceptional practical value and strong potential for widespread applications.展开更多
Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(...Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(ML)and deep learning(DL)methodologies are utilised for the robust detection and prediction of lung tumours.Recently,multi modal imaging emerged as a robust technique for lung tumour detection by combining various imaging features.To cope with that,we propose a novel multi modal imaging technique named versatile scale malleable image integration and patch wise attention network(VSMI2−PANet)which adopts three imaging modalities named computed tomography(CT),magnetic resonance imaging(MRI)and single photon emission computed tomography(SPECT).The designed model accepts input from CT and MRI images and passes it to the VSMI2 module that is composed of three sub-modules named image cropping module,scale malleable convolution layer(SMCL)and PANet module.CT and MRI images are subjected to image cropping module in a parallel manner to crop the meaningful image patches and provide them to the SMCL module.The SMCL module is composed of adaptive convolutional layers that investigate those patches in a parallel manner by preserving the spatial information.The output from the SMCL is then fused and provided to the PANet module.The PANet module examines the fused patches by analysing its height,width and channels of the image patch.As a result,it provides an output as high-resolution spatial attention maps indicating the location of suspicious tumours.The high-resolution spatial attention maps are then provided as an input to the backbone module which uses light wave transformer(LWT)for segmenting the lung tumours into three classes,such as normal,benign and malignant.In addition,the LWT also accepts SPECT image as input for capturing the variations precisely to segment the lung tumours.The performance of the proposed model is validated using several performance metrics,such as accuracy,precision,recall,F1-score and AUC curve,and the results show that the proposed work outperforms the existing approaches.展开更多
In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh enviro...In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh environment leads to significant variations in the shape and size of the defects.To address this challenge,we propose the multivariate time series segmentation network(MSSN),which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates.To tackle the classification difficulty caused by structural signal variance,MSSN employs logarithmic normalization to adjust instance distributions.Furthermore,it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences.Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95%localization and demonstrates the capture capability on the synthetic dataset.In a nuclear plant's heat transfer tube dataset,it captures 90%of defect instances with75%middle localization F1 score.展开更多
We propose a novel fast numerical calculation method for the Rayleigh-Sommerfeld diffraction integral,which is developed based on the existing scaled convolution method.This approach enables fast cal-culations for gen...We propose a novel fast numerical calculation method for the Rayleigh-Sommerfeld diffraction integral,which is developed based on the existing scaled convolution method.This approach enables fast cal-culations for general cases of off-axis scenarios where the sampling intervals and numbers of the input and observation planes are unequal.Additionally,it allows for arbitrary adjustment of the sampling interval of the impulse response function,facilitating a manual trade-off between computational load and accuracy.The er-rors associated with this method,which is equivalent to interpolation,primarily arise from the discontinuities of the sampling matrix of the impulse response function on its boundaries of periodic extension.To address this issue,we propose the concept of the padding function and its construction method,and evaluate its ef-fectiveness in enhancing computational accuracy.The feasibility of the proposed method is verified by nu-merical simulation and compared with the direct integration DI-method in a simplified scenario.It shows that the proposed method has good computational accuracy for the general case where the sampling interval of the input and observation plane is not equal under non-near-field diffraction,and when the diffraction distance is large,although the computational accuracy of the proposed method cannot exceed that of the DI-method,the computational amount can be significantly reduced with almost no effect on the computational accuracy.This method provides a general numerical calculation scheme of diffraction in the non-near field case for areas such as computational holography.展开更多
Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v...Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.展开更多
Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional a...Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.展开更多
Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the...Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.展开更多
Background:Coronary artery disease(CAD)is a major global health concern requiring efficient and accurate diagnostic methods.Manual interpretation of coronary computed tomography angiography(CTA)images is time-consumin...Background:Coronary artery disease(CAD)is a major global health concern requiring efficient and accurate diagnostic methods.Manual interpretation of coronary computed tomography angiography(CTA)images is time-consuming and prone to interobserver variability,underscoring the need for automated segmentation and stenosis detection tools.Methods:This study presents a hybrid multi-scale 3D segmentation framework utilizing both 3D U-Net and Enhanced 3D U-Net architectures,designed to balance computational efficiency and anatomical precision.Processed CTA images from the ImageCAS dataset underwent data standardization,normalization,and augmentation.The framework applies ensemble learning to merge coarse and fine segmentation masks,followed by advanced post-processing techniques,including connected component analysis and centerline extraction,to refine vessel delineation.Stenosis regions are detected using the Enhanced 3D U-Net and morphological operations for accurate localization.Results:The proposed pipeline achieved near-perfect segmentation accuracy(0.9993)and a Dice similarity coefficient of 0.8539 for coronary artery delineation.Precision,recall,and F1 scores for stenosis detection were 0.8418,0.8289,and 0.8397,respectively.The dual-model approach demonstrated robust performance across varied anatomical structures and effectively localized stenotic regions,indicating clear superiority over conventional models.Conclusion:This hybrid framework enables highly reliable and automated coronary artery segmentation and stenosis detection from 3D CTA images.By reducing reliance on manual interpretation and enhancing diagnostic consistency,the proposed method holds strong potential to improve clinical workflows for CAD diagnosis and management.展开更多
This study addresses the persistent scarcity of systematic and comparable data on mountain tourism,with particular reference to Northern Italy,as highlighted by FAO/UNWTO reports and recent academic literature.It aims...This study addresses the persistent scarcity of systematic and comparable data on mountain tourism,with particular reference to Northern Italy,as highlighted by FAO/UNWTO reports and recent academic literature.It aims to contribute to this gap by analyzing tourist flows,socio-demographic characteristics,preferences,and behaviors of domestic visitors to the Italian Alps.Data were collected through a survey conducted between December 2023 and January 2024 among 1,218 residents of Northwest and Northeast Italy and Friuli Venezia Giulia,using a stratified sampling approach.Descriptive statistics and inferential analyses were employed to examine visitation patterns,while K-means clustering was applied to identify distinct segments of mountain tourists based on activity preferences and motivations.Overall,82.5%of respondents reported visiting Alpine areas.Chi-square tests revealed statistically significant differences in visitation behavior according to age,occupational status,and income.Notably,spiritual activities,such as pilgrimages,elicited levels of interest comparable to those of more traditional mountain sports.The cluster analysis identified three visitor profiles:Active Young Enthusiasts,characterized by high engagement in multiple outdoor activities and motivated by psychological well-being and cultural enrichment;Well-being-Oriented Walkers,preferring low-intensity activities primarily driven by psychological relaxation;and Hiking-Oriented Explorers,exhibiting a strong propensity for mountain excursions associated with high levels of psychophysical well-being.These findings enhance understanding of the heterogeneous structure of mountain tourism demand in Northern Italy and offer insights relevant to sustainable destination planning and management in Alpine regions.展开更多
Arbitration is a key non-litigation commercial mechanism for the resolution of disputes, and the quality and credibility of its awards depend largely on the competency of the arbitrators. However, the selection and ev...Arbitration is a key non-litigation commercial mechanism for the resolution of disputes, and the quality and credibility of its awards depend largely on the competency of the arbitrators. However, the selection and evaluation systems for arbitrators in China have long faced challenges such as the vague criteria for competency and an unclear professionalization path for arbitrators. To address these issues, this study is grounded in the context of actual Chinese arbitration practice and based on the competency iceberg model. Through a methodological approach encompassing literature reviews, behavioral event interviews, expert revisions, and questionnaire surveys, a Chinese Arbitrator Competency Scale was developed and validated in this study. Examination of the findings indicated that the scale needed to consist of five dimensions—communication and coordination, cognitive skills, ethical conduct, work motivation, and personality traits—and possess a total of 28 specific indicators. Confirmatory analysis of the factors demonstrates a good fit for the five-dimensional model, with each of the dimensions exhibiting high reliability and validity. This scale is innovative in integrating the competency elements with Chinese characteristics, such as commercial acumen, crosscultural mediation skills, and adaptability to the local rule of law. This research not only enriches the competency theory in regard to the field of human resource management but also provides a scientific framework of standards and measurement tools for the selection, training, and evaluation of arbitrators. It thus has significant practical value for enhancing the professionalism and international competitiveness of China's arbitration system.展开更多
文摘Nonlinear analysis of heart rate variability （HRV） has become important as heart behaves as a complex system. In this work, the approximate entropy （ApEn） has been used as a nonlinear measure. A new concept of estimating the ApEn in different segments of long length of the recorded data called modified multiple scale （segment） entropy （MMPE） is introduced. The idea of estimating the approximate entropy in different segments is useful to detect the nonlinear dynamics of the heart present in the entire length of data. The present work has been carried out for three cases namely the normal healthy heart （NHH） data, congestive heart failure （CHF） data and Atrial fibrillation （AF） data and the data are analyzed using MMPE techniques. It is observed that the mean value of ApEn for NHH data is much higher than the mean values for CHF data and AF data. The ApEn profiles of CHF, AF and NHH data for different segments obtained using MPE profiles measures the heart dynamism for the three different cases. Also the power spectral density is obtained using fast fourier transform （FFT） analysis and the ratio of LF/HF （low frequency/high frequency） power are computed on multiple scales/segments namely MPLH （multiple scale low frequency to high frequency） for the NHH data, CHF data and AF data and analyzed using MPLH techniques. The results are presented and discussed in the paper.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金supported by the National Key Research and Development of China(No.2022YFB2503400).
文摘Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.
文摘Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
基金supported by the National Key R&D Program of China(No.2022YFC2504403)the National Natural Science Foundation of China(No.62172202)+1 种基金the Experiment Project of China Manned Space Program(No.HYZHXM01019)the Fundamental Research Funds for the Central Universities from Southeast University(No.3207032101C3)。
文摘Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.
基金supported by the Guangdong Basic and Applied Basic Research Foundation(2024A1515010987)the Medical Scientific Research Foundation of Guangdong Province(B2024035).
文摘Background:Diabetic retinopathy(DR)is one of the primary causes of visual impairment globally,resulting from microvascular abnormalities in the retina.Accurate segmentation of retinal blood vessels from fundus images plays a pivotal role in the early diagnosis,progression monitoring,and treatment planning of DR and related ocular conditions.Traditional convolutional neural networks often struggle with capturing the intricate structures of thin vessels under varied illumination and contrast conditions.Methods:In this study,we propose an improved U-Net-based framework named MSAC U-Net,which enhances feature extraction and reconstruction through multiscale and attention-based modules.Specifically,the encoder replaces standard convolutions with a Multiscale Asymmetric Convolution(MSAC)block,incorporating parallel 1×n,n×1,and n×n kernels at different scales(3×3,5×5,7×7)to effectively capture fine-grained vascular structures.To further refine spatial representation,skip connections are utilized,and the decoder is augmented with dual activation strategies,Squeeze-and-Excitation blocks,and Convolutional Block Attention Modules for improved contextual understanding.Results:The model was evaluated on the publicly available DRIVE dataset.It achieved an accuracy of 96.48%,sensitivity of 88.31%,specificity of 97.90%,and an AUC of 98.59%,demonstrating superior performance compared to several state-of-the-art segmentation methods.Conclusion:The proposed MSAC U-Net provides a robust and accurate approach for retinal vessel segmentation,offering substantial clinical value in the early detection and management of diabetic retinopathy.Its design contributes to enhanced segmentation reliability and may serve as a foundation for broader applications in medical image analysis.
基金supported by Natural Science Foundation Programme of Gansu Province(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Science and Technology Plan Key Research and Development Program Project(No.24YFFA024).
文摘Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.
基金supported in part by the National Natural Science Foundation of China under Grant 62201201the Foundation of Henan Educational Committee under Grant 242102211042.
文摘Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes.To overcome these limitations,we introduce MSAMamba-UNet,a lightweight model that integrates two novel architectures:Multi-Scale Mamba(MSMamba)and Adaptive Dynamic Gating Block(ADGB).MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets.ADGB dynamically selects convolutional kernels with varying receptive fields based on input features,improving the model’s capacity to accommodate diverse lesion textures and scales.Additionally,we introduce a Mix Attention Fusion Block(MAF)to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms.Extensive evaluation of MSAMamba-UNet on the ISIC 2016,ISIC 2017,and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs.Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%,85.47%,and 82.22%,as well as DSC scores of 92.20%,92.17%,and 90.24%,respectively.These results underscore the lightweight design and effectiveness of MSAMamba-UNet.
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
基金supported by the National Natural Science Foundation of China(62162007)the Guizhou Provincial Basic Research Program(Natural Science)(No.QianKeHeJiChu-ZK[2024]YiBan079).
文摘Semantic segmentation has made significant breakthroughs in various application fields,but achieving both accurate and efficient segmentation with limited computational resources remains a major challenge.To this end,we propose CGMISeg,an efficient semantic segmentation architecture based on a context-guided multi-scale interaction strategy,aiming to significantly reduce computational overhead while maintaining segmentation accuracy.CGMISeg consists of three core components:context-aware attention modulation,feature reconstruction,and crossinformation fusion.Context-aware attention modulation is carefully designed to capture key contextual information through channel and spatial attention mechanisms.The feature reconstruction module reconstructs contextual information from different scales,modeling key rectangular areas by capturing critical contextual information in both horizontal and vertical directions,thereby enhancing the focus on foreground features.The cross-information fusion module aims to fuse the reconstructed high-level features with the original low-level features during upsampling,promoting multi-scale interaction and enhancing the model’s ability to handle objects at different scales.We extensively evaluated CGMISeg on ADE20K,Cityscapes,and COCO-Stuff,three widely used datasets benchmarks,and the experimental results show that CGMISeg exhibits significant advantages in segmentation performance,computational efficiency,and inference speed,clearly outperforming several mainstream methods,including SegFormer,Feedformer,and SegNext.Specifically,CGMISeg achieves 42.9%mIoU(Mean Intersection over Union)and 15.7 FPS(Frames Per Second)on the ADE20K dataset with 3.8 GFLOPs(Giga Floating-point Operations Per Second),outperforming Feedformer and SegNeXt by 3.7%and 1.8%in mIoU,respectively,while also offering reduced computational complexity and faster inference.CGMISeg strikes an excellent balance between accuracy and efficiency,significantly enhancing both computational and inference performance while maintaining high precision,showcasing exceptional practical value and strong potential for widespread applications.
基金supported by the VTT Technical Research Centre of Finland and the work of Nayef Alqahtani is supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(Grant KFU251882).
文摘Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(ML)and deep learning(DL)methodologies are utilised for the robust detection and prediction of lung tumours.Recently,multi modal imaging emerged as a robust technique for lung tumour detection by combining various imaging features.To cope with that,we propose a novel multi modal imaging technique named versatile scale malleable image integration and patch wise attention network(VSMI2−PANet)which adopts three imaging modalities named computed tomography(CT),magnetic resonance imaging(MRI)and single photon emission computed tomography(SPECT).The designed model accepts input from CT and MRI images and passes it to the VSMI2 module that is composed of three sub-modules named image cropping module,scale malleable convolution layer(SMCL)and PANet module.CT and MRI images are subjected to image cropping module in a parallel manner to crop the meaningful image patches and provide them to the SMCL module.The SMCL module is composed of adaptive convolutional layers that investigate those patches in a parallel manner by preserving the spatial information.The output from the SMCL is then fused and provided to the PANet module.The PANet module examines the fused patches by analysing its height,width and channels of the image patch.As a result,it provides an output as high-resolution spatial attention maps indicating the location of suspicious tumours.The high-resolution spatial attention maps are then provided as an input to the backbone module which uses light wave transformer(LWT)for segmenting the lung tumours into three classes,such as normal,benign and malignant.In addition,the LWT also accepts SPECT image as input for capturing the variations precisely to segment the lung tumours.The performance of the proposed model is validated using several performance metrics,such as accuracy,precision,recall,F1-score and AUC curve,and the results show that the proposed work outperforms the existing approaches.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0608100)the National Natural Science Foundation of China(62332017,U22A2022)
文摘In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh environment leads to significant variations in the shape and size of the defects.To address this challenge,we propose the multivariate time series segmentation network(MSSN),which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates.To tackle the classification difficulty caused by structural signal variance,MSSN employs logarithmic normalization to adjust instance distributions.Furthermore,it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences.Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95%localization and demonstrates the capture capability on the synthetic dataset.In a nuclear plant's heat transfer tube dataset,it captures 90%of defect instances with75%middle localization F1 score.
文摘We propose a novel fast numerical calculation method for the Rayleigh-Sommerfeld diffraction integral,which is developed based on the existing scaled convolution method.This approach enables fast cal-culations for general cases of off-axis scenarios where the sampling intervals and numbers of the input and observation planes are unequal.Additionally,it allows for arbitrary adjustment of the sampling interval of the impulse response function,facilitating a manual trade-off between computational load and accuracy.The er-rors associated with this method,which is equivalent to interpolation,primarily arise from the discontinuities of the sampling matrix of the impulse response function on its boundaries of periodic extension.To address this issue,we propose the concept of the padding function and its construction method,and evaluate its ef-fectiveness in enhancing computational accuracy.The feasibility of the proposed method is verified by nu-merical simulation and compared with the direct integration DI-method in a simplified scenario.It shows that the proposed method has good computational accuracy for the general case where the sampling interval of the input and observation plane is not equal under non-near-field diffraction,and when the diffraction distance is large,although the computational accuracy of the proposed method cannot exceed that of the DI-method,the computational amount can be significantly reduced with almost no effect on the computational accuracy.This method provides a general numerical calculation scheme of diffraction in the non-near field case for areas such as computational holography.
基金the National Natural Science Foundation of China(No.62266025)。
文摘Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.
基金Open Access funding provided by the National Institutes of Health(NIH)The funding for this project was provided by NCATS Intramural Fund.
文摘Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.
文摘Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.
文摘Background:Coronary artery disease(CAD)is a major global health concern requiring efficient and accurate diagnostic methods.Manual interpretation of coronary computed tomography angiography(CTA)images is time-consuming and prone to interobserver variability,underscoring the need for automated segmentation and stenosis detection tools.Methods:This study presents a hybrid multi-scale 3D segmentation framework utilizing both 3D U-Net and Enhanced 3D U-Net architectures,designed to balance computational efficiency and anatomical precision.Processed CTA images from the ImageCAS dataset underwent data standardization,normalization,and augmentation.The framework applies ensemble learning to merge coarse and fine segmentation masks,followed by advanced post-processing techniques,including connected component analysis and centerline extraction,to refine vessel delineation.Stenosis regions are detected using the Enhanced 3D U-Net and morphological operations for accurate localization.Results:The proposed pipeline achieved near-perfect segmentation accuracy(0.9993)and a Dice similarity coefficient of 0.8539 for coronary artery delineation.Precision,recall,and F1 scores for stenosis detection were 0.8418,0.8289,and 0.8397,respectively.The dual-model approach demonstrated robust performance across varied anatomical structures and effectively localized stenotic regions,indicating clear superiority over conventional models.Conclusion:This hybrid framework enables highly reliable and automated coronary artery segmentation and stenosis detection from 3D CTA images.By reducing reliance on manual interpretation and enhancing diagnostic consistency,the proposed method holds strong potential to improve clinical workflows for CAD diagnosis and management.
基金funded by the European Union—Next Generation EU,in the framework of the consortium i NEST—Interconnected Nord-Est Innovation Ecosystem(PNRR,Missione 4 Componente 2,Investimento 1.5 D.D.105823 June 2022,ECS_00000043—Spoke1,RT2,CUP I43C22000250006)。
文摘This study addresses the persistent scarcity of systematic and comparable data on mountain tourism,with particular reference to Northern Italy,as highlighted by FAO/UNWTO reports and recent academic literature.It aims to contribute to this gap by analyzing tourist flows,socio-demographic characteristics,preferences,and behaviors of domestic visitors to the Italian Alps.Data were collected through a survey conducted between December 2023 and January 2024 among 1,218 residents of Northwest and Northeast Italy and Friuli Venezia Giulia,using a stratified sampling approach.Descriptive statistics and inferential analyses were employed to examine visitation patterns,while K-means clustering was applied to identify distinct segments of mountain tourists based on activity preferences and motivations.Overall,82.5%of respondents reported visiting Alpine areas.Chi-square tests revealed statistically significant differences in visitation behavior according to age,occupational status,and income.Notably,spiritual activities,such as pilgrimages,elicited levels of interest comparable to those of more traditional mountain sports.The cluster analysis identified three visitor profiles:Active Young Enthusiasts,characterized by high engagement in multiple outdoor activities and motivated by psychological well-being and cultural enrichment;Well-being-Oriented Walkers,preferring low-intensity activities primarily driven by psychological relaxation;and Hiking-Oriented Explorers,exhibiting a strong propensity for mountain excursions associated with high levels of psychophysical well-being.These findings enhance understanding of the heterogeneous structure of mountain tourism demand in Northern Italy and offer insights relevant to sustainable destination planning and management in Alpine regions.
基金Research on Problems and Countermeasures in Building the Capacity of the Grassroots International Chambers of Commerce in the Context of High-Quality Development (W2024H03841)a key research project of the China Council for the Promotion of International Trade in 2025。
文摘Arbitration is a key non-litigation commercial mechanism for the resolution of disputes, and the quality and credibility of its awards depend largely on the competency of the arbitrators. However, the selection and evaluation systems for arbitrators in China have long faced challenges such as the vague criteria for competency and an unclear professionalization path for arbitrators. To address these issues, this study is grounded in the context of actual Chinese arbitration practice and based on the competency iceberg model. Through a methodological approach encompassing literature reviews, behavioral event interviews, expert revisions, and questionnaire surveys, a Chinese Arbitrator Competency Scale was developed and validated in this study. Examination of the findings indicated that the scale needed to consist of five dimensions—communication and coordination, cognitive skills, ethical conduct, work motivation, and personality traits—and possess a total of 28 specific indicators. Confirmatory analysis of the factors demonstrates a good fit for the five-dimensional model, with each of the dimensions exhibiting high reliability and validity. This scale is innovative in integrating the competency elements with Chinese characteristics, such as commercial acumen, crosscultural mediation skills, and adaptability to the local rule of law. This research not only enriches the competency theory in regard to the field of human resource management but also provides a scientific framework of standards and measurement tools for the selection, training, and evaluation of arbitrators. It thus has significant practical value for enhancing the professionalism and international competitiveness of China's arbitration system.
