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FragSAM:Near real-time rock fragment segmentation for size distribution analysis across diverse engineering domains 认领 引用
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作者 Yudi Tang Yulin Wang +4 位作者 Jixiong Zhang Runzhe Hu Changwei Wang Joung Oh Guangyao Si 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第6期1167-1188,共22页
Rock fragment size distribution(FSD)plays an important role in various engineering applications,such as mining,tunnelling,and other underground construction scenarios.While vision-based deep learning approaches have b... Rock fragment size distribution(FSD)plays an important role in various engineering applications,such as mining,tunnelling,and other underground construction scenarios.While vision-based deep learning approaches have been increasingly applied to FSD analysis,they are often case-specific,showing limited cross-site generalization despite their accuracy.To address these challenges,FragSAM,an end-to-end,fully automated framework is proposed for near real-time rock fragment segmentation and FSD analysis across diverse engineering environments.FragSAM integrates the generalization power of Segment Anything Model(SAM)with a context-aware prompting mechanism and lightweight architecture for efficient dense fragment segmentation.In Stage 1,an enhanced SAM automatically generates high-quality annotations,which are used to train a modified CenterNet for precise centroid prediction.In Stage 2,these centroids serve as prompts for EdgeSAM,a lightweight SAM variant optimized for real-time inference.This two-stage design eliminates dense grid prompting and reduces reliance on heavy postprocessing,enabling efficient and scalable segmentation.Experimental results show that FragSAM achieves competitive segmentation performance with significantly lower latency and model complexity compared to existing SAM-based methods.In comparison with supervised learning approaches,it also demonstrates superior generalization and performs better in low-quality or unseen scenarios.Furthermore,case studies on blasting fragmentation,TBM muck,and coastal rock surfaces confirm its robustness and seamless cross-site adaptability,requiring no tuning or retraining,making it highly practical for on-site applications. 展开更多
关键词 Segment anything model Rock fragment size distribution FragSAM Prompt-based vision models Near real-time segmentation
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A Novel Segment-switching Strategy with the Current Transient Overshoot Suppression for Segmented Long-stator Linear Motor 认领 引用
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作者 Yanfei Li Zixin Li +4 位作者 Hang Zhang Fei Xu Cong Zhao Fanqiang Gao Yaohua Li 《CES Transactions on Electrical Machines and Systems》 EI CSCD 2026年第2期188-203,共16页
The segmented power supply scheme for long-stator linear motor facilitates reducing power capacity and achieving a high power factor.However,the segment-switching process leads to overcurrent under high-speed conditio... The segmented power supply scheme for long-stator linear motor facilitates reducing power capacity and achieving a high power factor.However,the segment-switching process leads to overcurrent under high-speed conditions.This paper proposes a novel segment-switching strategy based on the time-optimal control theory.It employs time-optimal feedforward voltage and planned current trajectory during the switching transient process.Thus,it ensures rapid disconnection of the exiting segment and rapid establishment of the current in the incoming segment,while suppressing transient current overshoot.The mathematical model of the long-stator linear motor is established in the process of segment-switching.It derives the minimum times required to force the exiting segment current to zero and to establish the incoming segment current to the reference value by time-optimal control theory.Furthermore,the time-optimal voltages and current trajectories are calculated.The timeoptimal current trajectories are used as the reference command for the current loop.The time-optimal feedforward voltages are introduced into the current loop control.Hence,it achieves rapid disconnection of the exiting segment and fast,accurate establishment of the incoming segment current.Experimental and simulation results collectively validate the effectiveness of the proposed segment-switching strategy. 展开更多
关键词 Long-stator linear motor Segmented power supply Segment switching Longitudinal static end-effect Timeoptimal control
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An intelligent segmentation method for leakage points in central serous chorioretinopathy based on fluorescein angiography images 认领 引用
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作者 Jian-Guo Xu Yong-Chi Liu +4 位作者 Fen Zhou Jian-Xin Shen Zhi-Peng Yan Xin-Ya Hu Wei-Hua Yang 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第3期421-433,共13页
AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigat... AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigational laser equipment.METHODS:A dataset with dual labels(point-level and pixel-level)was first established based on fundus fluorescein angiography(FFA)images of CSC and subsequently divided into training(102 images),validation(40 images),and test(40 images)datasets.An intelligent segmentation method was then developed,based on the You Only Look Once version 8 Pose Estimation(YOLOv8-Pose)model and segment anything model(SAM),to segment CSC leakage points.Next,the YOLOv8-Pose model was trained for 200 epochs,and the best-performing model was selected to form the optimal combination with SAM.Additionally,the classic five types of U-Net series models[i.e.,U-Net,recurrent residual U-Net(R2U-Net),attention U-Net(AttU-Net),recurrent residual attention U-Net(R2AttUNet),and nested U-Net(UNet++)]were initialized with three random seeds and trained for 200 epochs,resulting in a total of 15 baseline models for comparison.Finally,based on the metrics including Dice similarity coefficient(DICE),intersection over union(IoU),precision,recall,precisionrecall(PR)curve,and receiver operating characteristic(ROC)curve,the proposed method was compared with baseline models through quantitative and qualitative experiments for leakage point segmentation,thereby demonstrating its effectiveness.RESULTS:With the increase of training epochs,the mAP50-95,Recall,and precision of the YOLOv8-Pose model showed a significant increase and tended to stabilize,and it achieved a preliminary localization success rate of 90%(i.e.,36 images)for CSC leakage points in 40 test images.Using manually expert-annotated pixel-level labels as the ground truth,the proposed method achieved outcomes with a DICE of 57.13%,an IoU of 45.31%,a precision of 45.91%,a recall of 93.57%,an area under the PR curve(AUC-PR)of 0.78 and an area under the ROC curve(AUC-ROC)of 0.97,which enables more accurate segmentation of CSC leakage points.CONCLUSION:By combining the precise localization capability of the YOLOv8-Pose model with the robust and flexible segmentation ability of SAM,the proposed method not only demonstrates the effectiveness of the YOLOv8-Pose model in detecting keypoint coordinates of CSC leakage points from the perspective of application innovation but also establishes a novel approach for accurate segmentation of CSC leakage points through the“detect-then-segment”strategy,thereby providing a potential auxiliary means for the automatic and precise realtime localization of leakage points during traditional laser photocoagulation for CSC. 展开更多
关键词 You Only Look Once version 8 Pose Estimation segment anything model central serous chorioretinopathy leakage point segmentation
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An enhanced segmentation method for 3D point cloud of tunnel support system using PointNet++t and coverage-voted strategy algorithms 认领 引用 被引量：3
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作者 Wenju Liu Fuqiang Gao +4 位作者 Shuangyong Dong Xiaoqing Wang Shuwen Cao Wanjie Wang Xiaomin Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1653-1660,共8页
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
关键词 Point cloud segmentation Improved PointNet++ Tunnel laser scanning Rock bolt automatic recognition
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Precision organoid segmentation technique(POST):accurate organoid segmentation in challenging bright-field images 认领 引用 被引量：2
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作者 Xuan Du Yuchen Li +5 位作者 Jiaping Song Zilin Zhang Jing Zhang Yanhui Li Zaozao Chen Zhongze Gu 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2026年第1期80-93,I0013-I0016,共14页
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
关键词 Organoid Drug screening Deep learning Image segmentation
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Optimizing foreign fiber segmentation performance with DeepLab V3+and GAN in industrial IoE environments 认领 引用 被引量：1
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作者 Shuo Yang Jingbin Li +5 位作者 Yang Li Jing Nie Dian Guo Liqing Hu Yugang Feng Liansheng Zhang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期505-519,共15页
In industrial Internet of Everything(IoE)environments,the precise detection of tiny foreign fibers on the surface of bobbin yarns is crucial for ensuring the quality of textile products.However,detecting these fibers ... In industrial Internet of Everything(IoE)environments,the precise detection of tiny foreign fibers on the surface of bobbin yarns is crucial for ensuring the quality of textile products.However,detecting these fibers often exceeds the capabilities of both human vision and existing automation equipment.To address this challenge,this research proposes a novel foreign fiber segmentation method that integrates Generative Adversarial Networks(GANs)with an enhanced encoder-decoder architecture,significantly improving detection accuracy in industrial IoE scenarios.Specifically,we develop a dual-path attention encoding network that synergistically combines MobileNetV2’s computational efficiency with ContextNet’s multi-scale contextual awareness,thereby enhancing the extraction of contextual features for tiny foreign fibers.A hybrid channel-spatial attention mechanism is designed by parallel integration of channel-wise excitation and spatial attention mapping,which substantially strengthens the capture of discriminative features for tiny foreign fibers in high-resolution images.The decoding stage employs dense skip-connections to construct multi-scale feature propagation paths,optimizing detail preservation during upsampling processes.To tackle the severe class imbalance in fiber-background pixel distribution,this research introduces a Weighted Binary Cross-Entropy(WBCE)loss function with adaptive focal weighting.Experimental results demonstrate that the proposed DeepLab-DPA framework achieves 98.77%Accuracy,85.93%MIoU,and balanced performance metrics(87.01%Precision,86.84%Recall,86.92%F1-Score),confirming its effectiveness for industrial fiber detection tasks. 展开更多
关键词 Industrial IoE Data generation Foreign fiber Semantic segmentation Attention mechanism
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RCTUnet:a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction 认领 引用 被引量：1
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作者 Ting LI Yang LIU +10 位作者 Haikuan FENG Meiyan SHU Hao YANG Yuanyuan FU Xin XU Yinghao LIN Hongbo QIAO Wei GUO Xinming MA Lei SHI Jibo YUE 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期517-536,共20页
Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between ... Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between fragmented residue and soil,compounded by variable illumination and shadows in field imagery,often lead to poor segmentation performance.To overcome these limitations,we introduce RCTUnet,a novel deep learning architecture designed for robust crop-residue-soil segmentation and precise CRC estimation.RCTUnet’s architecture synergistically integrates three key components:(1)a ResNet50 backbone for deep,multi-scale feature extraction;(2)a convolutional block attention module(CBAM)to adaptively focus on salient residue features across both channel and spatial dimensions;and(3)a transformer-based global context fusion module(GCFM)to model long-range spatial dependencies,which is critical for interpreting heterogeneous residue patterns.We evaluated RCTUnet on a dataset of 1220 field-acquired images spanning four typical crop rotations.Experimental results show that,compared to traditional models:(1)RCTUnet achieves significantly higher crop-residue-soil segmentation accuracy than classic models including Unet,Unet++,DeepLabV3,segmentation network(SegNet),and fully convolutional network(FCN),with improvements of 3.24%,3.42%,4.88%,8.28%,and 6.05%in overall accuracy,respectively;(2)RCTUnet yields superior residue-soil segmentation performance,with increases in residue recall of 7.67%,7.37%,14.09%,27.05%,and 16.91%,respectively;(3)RCTUnet shows enhanced CRC estimation accuracy,achieving a root mean square error(RMSE)of 4.875,representing a 45.5%improvement over Unet(RMSE=8.941).These results demonstrate the efficacy of our hybrid approach,which combines deep hierarchical features,dual-domain attention,and global context modeling.RCTUnet provides a robust and reliable tool for automated CRC assessment,advancing the capabilities of in-field agricultural monitoring. 展开更多
关键词 Deep learning Crop residue cover Image segmentation Conservation tillage
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Visitor segmentation in alpine tourism:Evidence from a survey-based cluster analysis in northern Italy 认领 引用
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作者 Francesca VISINTIN Elisa TOMASINSIG +4 位作者 Laura PAGANI Ivana BASSI Vanessa DEOTTO Lucia MONTEFIORI Luca ISEPPI 《Journal of Mountain Science》 SCIE CSCD 2026年第2期738-754,共17页
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
关键词 Mountain tourism Visitor segmentation K-means clustering Tourist behavior Activity-based segmentation Italian Alps
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A Semantic Segmentation Network for Colorectal Polyp Images With Progressive Fusion of Dual-Branch Features 认领 引用
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作者 Tianxu Yan Jiabin Yu +6 位作者 Zheng Li Liangyu Chen Hongmei Mi Luyang Chen Wei Si Dongping Zhang Hui Lin 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期900-919,共20页
Accurate segmentation of colorectal polyps is essential for early colorectal cancer screening,yet remains challenging due to weak foreground–background contrast,disrupted boundaries caused by specular reflections and... Accurate segmentation of colorectal polyps is essential for early colorectal cancer screening,yet remains challenging due to weak foreground–background contrast,disrupted boundaries caused by specular reflections and intestinal folds,and pronounced scale variation among polyps.These factors make it difficult for existing methods to jointly preserve fine boundary details and robust global semantic context.To address these task‐specific challenges,we propose a Dual‐branch Feature Progressive Fusion Network(DFPF‐Net)for colorectal polyp segmentation.DFPF‐Net adopts a dual‐encoder architecture that integrates a CNN‐based encoder for local and boundary‐sensitive representation for global semantic modelling.A boundaryaware branch equipped with stacked Inversely Perceive Information Layers(IPILs)enhances ambiguous and fragmented contours,while the semantic branch incorporates Misalignment Fusion Modules(MFMs)and a Misaligned Single‐layer Reinforcement Module(MSRM)to alleviate semantic misalignment and insufficient cross‐scale interaction.Furthermore,a Perceptual Information Fusion Module(PIFM)enables effective semantic–boundary collaboration,and a Multi‐level Residual Decoding Module(MRDM)progressively reconstructs structurally consistent segmentation outputs.Extensive experiments on multiple public colonoscopy datasets demonstrate that DFPF‐Net achieves competitive and robust segmentation performance.In particular,on the challenging ETIS dataset,DFPF‐Net attains 0.785 mDice and 0.704 mIoU,indicating its capability in handling complex structures and ambiguous boundaries in colorectal polyp segmentation. 展开更多
关键词 boundary‐aware learning colorectal polyp segmentation multi‐scale fusion semantic segmentation vision transformer
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Efficient Dataset Generation for Stacked Meat Products Instance Segmentation in Food Automation 认领 引用
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作者 Hoang Minh Pham Anh Dong Le +2 位作者 Pablo Malvido-Fresnillo Saigopal Vasudevan JoséL.Martínez Lastra 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期224-226,共3页
Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for ... Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry. 展开更多
关键词 dataset generation segment anything model sam food automation raw meat productsa automating food production linesaccurate instance segmentation stacked meat products semi automatic annotation
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Unleashing the Potential of Generalist Segmentation Foundation Models for Biomedical Image and Video Analysis 认领 引用
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作者 Yichi Zhang Zhenrong Shen +2 位作者 Lanlan Li Wenbo Zhang Le Xue 《iRADIOLOGY》 CSCD 2026年第2期127-136,共10页
The unprecedented developments in generalist segmentation foundation models have become a dominant focus in the field of computer vision,introducing a multitude of previously unexplored capabilities in a wide range of... The unprecedented developments in generalist segmentation foundation models have become a dominant focus in the field of computer vision,introducing a multitude of previously unexplored capabilities in a wide range of natural image and video analysis tasks.From the pioneering segment anything model(SAM)that revolutionized prompt-driven image segmentation to the recent SAM2 which enables streaming video with robust spatiotemporal consistency,these models have demonstrated effective adaptability in natural scenarios and show strong potential for biomedical applications.In this paper,we present a comprehensive and in-depth review of the development,adaptation,and application of generalist segmentation foundation models in biomedical domains.We first contextualize the evolution of key models and their core mechanisms,highlighting their potential for bridging the gap between general vision and specialized biomedical tasks.We then systematically examine the challenges in applying these models to biomedical data,including domain shift,ambiguous boundaries,and dimensional gaps for 3D medical images.Finally,we articulate our perspectives on the future research directions.This review aims to provide a roadmap for researchers,facilitating the translation of generalist segmentation capabilities into effective biomedical solutions. 展开更多
关键词 foundation model medical image segmentation segment anything model
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Enhancing multiclass brain tumor classification through automated segmentation-guided deep learning 认领 引用
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作者 Pattaramon Vuttipittayamongkol Phakorn Charoenthiphakorn +2 位作者 Yarida Fuangfoo Pornnapha Na Phirot Thanawat Sanosiang 《Medical Data Mining》 CAS 2026年第2期15-33,共19页
Background:Accurate classification of brain tumors from Magnetic Resonance Imaging(MRI)is essential for clinical decision-making but remains challenging due to tumor heterogeneity.Existing approaches often focus solel... Background:Accurate classification of brain tumors from Magnetic Resonance Imaging(MRI)is essential for clinical decision-making but remains challenging due to tumor heterogeneity.Existing approaches often focus solely on classification or treat segmentation and classification as separate tasks,limiting overall performance and interpretability.Methods:This study proposes an end-to-end automated framework that integrates optimized tumor localization with multiclass classification.An optimized segmentation model is first employed to generate tumor masks,which are then overlaid on MRI scans to produce attention-enhanced inputs.These inputs are subsequently used to train a convolutional neural network(CNN)classifier.Experiments were conducted on a public dataset comprising 4,237 MRI scans across four categories:normal,glioma,meningioma,and pituitary tumors.Results:Three widely used segmentation models were systematically evaluated,with an optimized U-Net achieving the best performance(accuracy=0.9939,Dice=0.8893).Segmentation-guided classification consistently improved performance across six CNN architectures,with the most notable gains observed in heterogeneous tumor types such as glioma and meningioma.Among the classifiers,EfficientNet-V2 achieved the highest performance,with an accuracy of 0.9835,precision of 0.9858,recall of 0.9804,and F1-score of 0.9828.The framework was further validated on an independent external dataset,demonstrating consistent performance and robustness across diverse MRI sources.Conclusion:The proposed framework demonstrates strong potential for multiclass brain tumor classification by effectively combining segmentation and classification.This segmentation-driven approach not only enhances predictive accuracy but also improves interpretability,making it more suitable for clinical applications. 展开更多
关键词 brain tumor classification MRI segmentation segmentation-guided CNN multiclass classification tumor localization medical imaging
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Subnetwork-based federated few-shot semantic segmentation of organ images 认领 引用
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作者 Junpeng WU Meng ZHAO Huanping ZHANG 《Optoelectronics Letters》 EI 2026年第5期275-281,共7页
Federated learning(FL),as a distributed learning paradigm,allows multiple medical institutions to collaborate on learning without the need to centralize all client data.However,existing methods pay little attention to... Federated learning(FL),as a distributed learning paradigm,allows multiple medical institutions to collaborate on learning without the need to centralize all client data.However,existing methods pay little attention to more challenging medical image semantic segmentation tasks,especially in the scenario of the imbalanced dataset in federated few-shot learning(FSL).In this paper,we propose a subnetwork-based federated few-shot organ image segmentation method.Firstly,individual clients train using local training samples and then upload local model gradients to the server.The server utilizes their respective local model gradients to update the subnetwork maintained on the server and generate aggregation weights for forming personalized model parameters.Through this method,we can learn the similarities between different clients to address data heterogeneity issues.In addition,to enhance the communication efficiency between clients and the server,we have also designed a personalized layer aggregation strategy,which only transmits partial layer model parameters during the communication process to improve communication efficiency.Finally,we conducted experiments on abdomen magnetic resonance imaging(ABD-MRI)and abdomen computed tomography(ABD-CT)datasets to demonstrate the effectiveness of our method. 展开更多
关键词 subnetwork based federated learning semantic segmentation few shot learning imbalanced dataset medical image semantic segmentation local training federated learning fl collaborate learning
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Different approaches of laparoscopic anatomic hepatectomy of segment 7 for hepatocellular carcinoma:A multicenter study 认领 引用
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作者 Xing-Ru Wang Qi-Fan Zhang +6 位作者 Wei Cheng Xiao Liang Jun Cao Yong-Gang Wei Jian-Wei Li Hong-Guang Wang Chinese Research Group for Minimally Invasive Anatomical Liver Resection 《Hepatobiliary & Pancreatic Diseases International》 SCIE CAS CSCD 2026年第1期42-51,共10页
Background:Laparoscopic anatomic hepatectomy of segment 7(LAH-S7)is a challenging surgery.In this study we aimed to investigate surgical and oncological outcomes of various approaches of LAH-S7 in patients with hepato... Background:Laparoscopic anatomic hepatectomy of segment 7(LAH-S7)is a challenging surgery.In this study we aimed to investigate surgical and oncological outcomes of various approaches of LAH-S7 in patients with hepatocellular carcinoma(HCC).A particular focus was placed on identifying the Glissonean pedicle of segment 7(G7)and the intersegmental plane.Given the scarcity of comprehensive reviews or comparative studies on clinical outcomes,we also sought to analyze the experiences and advantages associated with different approaches in relation to the anatomic variations of G7.Methods:The clinical data of 124 patients who underwent LAH-S7 for HCC across seven tertiary referral medical centers in China were retrospectively analyzed.Three surgical approaches were categorized based on the procedures used for G7 identification:the indocyanine green(ICG)fluorescence positive staining approach(IFPA),the Glissonean approach(GA),and the hepatic vein-guided approach(HVGA).Subsequently,the postoperative short-term results and oncological outcomes of the three different approaches were compared.Results:The distribution of surgical approaches among the patients was as follows:IFPA in 16(12.9%),GA in 62(50.0%),and HVGA in 46(37.1%)patients.Complications were observed in 27(21.8%)patients.The 1-,3-,and 5-year overall survival(OS)rates were 99.1%,89.2%,and 84.7%,respectively.The 1-,3-,and 5-year recurrence-free survival(RFS)rates were 99.0%,84.7%,and 69.3%,respectively.The OS and RFS rates were comparable across the three approaches.Conclusions:Following a standardized surgical procedure,LAH-S7 is demonstrated to be safe and yields favorable oncological outcomes.Surgeons performing LAH-S7 should select the appropriate surgical approach based on the anatomical characteristics and variations of G7. 展开更多
关键词 Hepatocellular carcinoma Liver neoplasms Hepatectomy Laparoscopy Indocyanine green Segment 7
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Soft-segment-engineered Self-healing Polyurethane for High-performance Electrorheological Elastomers and Capacitive Sensors 认领 引用
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作者 Yan‑Bo Yang Yao Wan +4 位作者 Zi‑Xuan Zhang Zhen‑Jie Zhao Yong‑Ri Liang Hyoung Jin Choi Ying‑Dan Liu 《Chinese Journal of Polymer Science》 SCIE EI CAS CSCD 2026年第7期2269-2281,共13页
Polyurethane(PU)holds promise as a matrix for electrorheological elastomers(EREs)because of its excellent mechanical properties;however,its high modulus often limits electrorheological(ER)efficiency.This study address... Polyurethane(PU)holds promise as a matrix for electrorheological elastomers(EREs)because of its excellent mechanical properties;however,its high modulus often limits electrorheological(ER)efficiency.This study addresses this by tailoring the soft-segment architecture of PU to adjust its mechanical and dielectric properties,thus improving the ER response of the PU-based ERE.Dynamic covalent bonds have also been introduced to enable self-healing.Using poly(propylene glycol)(PPG),poly(tetramethylene glycol)(PTMG),and polycaprolactone(PCL)as the soft segments,we fabricated EREs with 20 wt%TiO2.The resulting PPG-ERE exhibited an outstanding ER effect of 229.4%at 3 kV/mm,along with a high stretchability(1835%elongation)and tensile strength of 3.6 MPa.PTMG-ERE has the highest storage modulus of 1.43 MPa at 3 kV/mm and a relatively high tensile strength of up to 6.5 MPa,which is attributed to enhanced hydrogen bonding interactions among the regular PTMG segments.The PCL-ERE with the highest Young's modulus resulted in the lowest ER efficiency of 48%because of its high crystallization tendency.The PPG-ERE also demonstrated efficient self-healing,recovering 79%of its mechanical strength after 12 h at room temperature.When applied in a capacitive pressure sensor,the PPG-ERE showed a fast response(220 ms)and recovery(90 ms),detecting forces as low as 3 N.This study provides a practical strategy for designing high-performance multifunctional EREs through soft-segment engineering and dynamic bonding. 展开更多
关键词 Electrorheological elastomer Polyurethane Soft segment Self-healing Capacitive pressure sensor
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An efficientself-adaptive planning algorithm for shield machine attitude and segment assembly considering machine-segment interaction 认领 引用
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作者 Gan Wang Qian Fang +3 位作者 Jun Wang Zhenhua Ru Jianying Wei Jinkun Huang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5109-5124,共16页
Both shield machine attitude and segment assembly play crucial roles in ensuring the construction quality of shield tunnels.This study proposes an efficientSelf-Adaptive Planning Algorithm(SAPA)for shield machine atti... Both shield machine attitude and segment assembly play crucial roles in ensuring the construction quality of shield tunnels.This study proposes an efficientSelf-Adaptive Planning Algorithm(SAPA)for shield machine attitude control and segment assembly,which jointly considers shield machine attitude planning and segment assembly point selection.A series of efficientthree-dimensional(3D)computational methods is developed for multiple key indicators,including distance deviation,angular deviation,cylinder stroke difference,tail clearance,and stagger-jointed assembly.With the developed selfadaptive weighting method,SAPA achieves a balanced optimization of all indicators,ensuring compliance with all control criteria during shield tunneling.Importantly,SAPA fully accounts for the interaction between the shield machine and segments during shield advancement and segment assembly.A series of analyses reveals that SAPA significantlyoutperforms the traditional fixed-weight method,primarily due to the self-adaptive weighting method.Among the evaluated optimization algorithms,Broyden-Fletcher-Goldfarb-Shanno(BFGS)algorithm demonstrates the best overall performance while satisfying engineering computational speed constraints.As the calculation interval increases,the overall performance of SAPA declines,while computational time decreases exponentially.A calculation interval of 0.1 m is recommended as it provides a favorable balance between accuracy and efficiency.Compared to the traditional trajectory correction method,SAPA enables fully autonomous trajectory correction with greater efficiency,effectively avoiding over-correction and reducing labor costs. 展开更多
关键词 Shield tunnel Shield machine attitude Segment assembly Self-adaptive Optimization
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Development and characterization of a new wheat-rye T6BS.6BL-6RLKusmall segment translocation with powdery mildew resistance and potential breeding value 认领 引用
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作者 Zongxiang Tang Ruiyu Zhang +4 位作者 Shuangjun Gong Guangrong Li Zujun Yang Wentao Dong Shulan Fu 《The Crop Journal》 SCIE CSCD 2026年第3期915-922,共8页
Rye(Secale cereale L.)contains numerous disease resistance genes that can be utilized for wheat(Triticum aestivum)improvement.For example,rye chromosome 6 carries powdery mildew resistance genes.Wheat-rye 6R transloca... Rye(Secale cereale L.)contains numerous disease resistance genes that can be utilized for wheat(Triticum aestivum)improvement.For example,rye chromosome 6 carries powdery mildew resistance genes.Wheat-rye 6R translocation lines are highly useful,but more wheat-rye 6R translocation lines with potential breeding value are needed.In this study,we identified a new wheat-rye T6 BS.6 BL-6 RLKutranslocation chromosome,conferring powdery mildew resistance,from the progeny of the irradiated wheat-rye 6 RLKuditelosomic addition line.Genotyping using the wheat GBW16K array and specific markers revealed that approximately 104.03 Mb of the distal segment of 6 RLKureplaced approximately6.1 Mb of the distal segment of the long arm of 6 B(6 BL)to form the translocation chromosome.OligoFISH painting indicated that the 104.03 Mb segment of 6 RLKuis homologous to homoeologous group 7 chromosomes.Using wheat cultivars Chuanmai 62(CM62)and Chuannong 32(CN32)as backcross parents,we transferred the T6 BS.6 BL-6 RLKuchromosome into the two wheat backgrounds.We selected two translocation lines:CM62-6 RL and CN32-6 RL.Genotyping analysis indicated that the CM62-6 RL and CN32-6 RL genomes are highly similar to those of CM62 and CN32,respectively.The grains of CM62-6 RL were shriveled,whereas those of CN32-6 RL were full.The effect of the T6 BS.6 BL-6 RLKuchromosome on grain width also depended on the genetic background of the wheat.The improved grain lengths of both CM62-6 RL and CN32-6 RL contributed to the improvement in their thousand-kernel weight.The translocation chromosome had no negative effects on other important agronomic traits.These findings highlight the potential breeding value of the wheat-rye 6R translocation chromosome T6 BS.6 BL-6 RLKu.The compensation mechanisms of this translocation chromosome in different wheat backgrounds deserve further study. 展开更多
关键词 Wheat Rye Chromosome 6R Powdery mildew Small segment translocation
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TransUNet framework improved computed tomography image segmentation for core pore evolution 认领 引用
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作者 Shao-Hua Zhou Tian-Bao Liu +6 位作者 Yu Zhao Shao-Hao Yin Zhi-Yu Liu Ji-Jun Liu Ling-Wei Du Yue-Tong Zhao Wei-Guang Shi 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3682-3697,共16页
The development of oil and gas is constrained by difficulties in dynamically characterizing pore structures.Traditional methods inadequately represent the complex interactions between mineral dissolution,precipitation... The development of oil and gas is constrained by difficulties in dynamically characterizing pore structures.Traditional methods inadequately represent the complex interactions between mineral dissolution,precipitation,and fluid flow.This study addresses these gaps by introducing a Transformer U-Neural Network(TransUNet)for computed tomography(CT)image segmentation.The integrated workflow combines conventional CT(Resolution of 5.4μm)and synchrotron radiation CT(Resolution of0.8μm)for dynamic flooding,imaging,segmentation,and precise 3D pore network extraction,overcoming resolution limits.TransUNet's strong global attention and feature extraction reduce overfitting and deliver high-accuracy segmentation of minerals,pores,and argillaceous microporous networks(AMN),achieving 74.92%intersection over union(IoU)for AMN.A porosity correction method improves conventional CT porosity accuracy to 94%of gas-measured values.Alkaline flooding experiments reveal:(1)initial clay swelling reduces small pore size by~50%as alkaline ions destabilize clay;(2)mineral dissolution,such as dolomite,creates secondary pores,increasing 80μm pores by 1.8 times;(3)silicate dissolution increases porosity and leads to a 93.7%rise in permeability.Clay reorganization enhances the AMN by 46.1%.The pore size distribution shifts to log-no rmal at steady state,and throat connectivity improves flow capacity.This work pioneers Transformer-based CT image segmentation,introduces cross-resolution prediction,and clarifies pore regulation by mineral phase changes,establishing a new paradigm for chemical flooding in sandstone reservoirs. 展开更多
关键词 Synchrotron radiation CT Image segmentation TransUNet Argillaceous microporous network Alkaline flooding porosity correction
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AGPLO-Driven Optimisation for Accurate Segmentation of Papillary Thyroid Carcinoma in Medical Imaging 认领 引用
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作者 Jing Ruan Xiaoxiao Chen +12 位作者 Hanbing Yao Yujia Xu Shiqi Xu Shihao Zhao Yulun Wu Yingting Dai Yubing Chen Shuqing Ma Qiongying Zhang Ying Zhou Ali Asghar Heidari Huiling Chen Yangping Shentu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期847-858,共12页
Papillary Thyroid Carcinoma(PTC)is the most prevalent thyroid malignancy,and accurate lesion segmentation is essential for clinical diagnosis and treatment planning.Metaheuristic optimisation algorithms have been wide... Papillary Thyroid Carcinoma(PTC)is the most prevalent thyroid malignancy,and accurate lesion segmentation is essential for clinical diagnosis and treatment planning.Metaheuristic optimisation algorithms have been widely used in Multi-Threshold Image Segmentation(MTIS),but many existing methods suffer from an imbalance between global exploration and local exploitation.This study aims to develop a robust and well-balanced optimisation algorithm to improve the accuracy and stability of MTIS for PTC images.An Adaptive Guided Polar Lights Optimisation(AGPLO)algorithm is proposed,which incorporates an adaptive phase-shift operator,magnetic guiding convergence,and energy burst exploration mechanisms to dynamically regulate search behaviour.AGPLO was evaluated on the IEEE CEC2017 benchmark suite and applied to Rényi entropy-based MTIS for PTC image segmentation.Experimental results on benchmark functions demonstrate that AGPLO outperforms several original and advanced metaheuristic algorithms in terms of convergence accuracy,stability,and robustness.In PTC image segmentation experiments,AGPLO achieves superior PSNR,SSIM,and FSIM values,producing clearer lesion boundaries and preserving structural details more effectively than comparative methods.The proposed AGPLO provides an effective and reliable optimisation framework for MTIS and shows strong potential for intelligent medical image analysis applications. 展开更多
关键词 AGPLO medical image segmentation metaheuristic algorithms papillary thyroid carcinoma
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Suitable area selection method based on scene matching level segmentation 认领 引用
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作者 Chao YANG Yuanxin YE +3 位作者 Renyuan LIU Chengjia FAN Liang ZHOU Jiwei DENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期356-369,共14页
The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which ... The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which is crucial for enhancing aircraft positioning accuracy.Traditional methods for image matchability analysis are often limited by their reliance on manual feature parameter design and threshold-based filtering,resulting in suboptimal accuracy and efficiency.This paper proposes a novel network architecture for selecting suitable navigation areas using image Matching Level Segmentation(MLSNet).The approach involves two key innovations:a method for generating segmentation labels that quantify matchability levels and an end-to-end network architecture for rapid and precise prediction of reference image matchability segmentation maps.The network includes two core modules:the saliency analysis module uses multi-layer convolutional networks to accurately detect image saliency features across various levels and scales;the multidimensional attention module utilizes attention mechanisms to focus on feature channels and spatial neighborhood scenes to assess the image’s matchability.Our method was rigorously tested on an extensive collection of remote sensing images,where it was benchmarked against a range of both traditional and cutting-edge deep learning methods.The findings indicate that MLSNet is significantly superior to traditional methods in accuracy and efficiency of matchability analysis,and is also relatively ahead of state-of-the-art deep learning models. 展开更多
关键词 Deep learning Image matching level segmentation Optical Scene matching navigation Suitable matching area selection
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