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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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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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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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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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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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Investigating strain localization at cracked concrete-sandstone specimens using optimized fractal theory-based image thresholding segmentation algorithm 认领 引用
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作者 Xiaojiang Deng Yu Zhao +5 位作者 Mingxuan Shen Jing Bi Chaolin Wang Yongfa Zhang Yang Li Lin Ning 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5542-5561,共20页
The stability of concrete-rock interfaces is a critical issue in underground engineering.This study investigated the strain localization mechanism and energy evolution of concrete-sandstone specimens containing single... The stability of concrete-rock interfaces is a critical issue in underground engineering.This study investigated the strain localization mechanism and energy evolution of concrete-sandstone specimens containing single and double interfacial cracks at various inclination angles.Acoustic emission(AE)technology and energy theory were used to analyze energy evolution,whereas digital image correlation was employed to examine strain development and fracture mechanisms.A new approach combining digital image processing and custom binarization was introduced to characterize the fractal properties of crack patterns using the box-counting method.Experimental results showed that the AE cumulative energy and stress-strain curves divided the loading process into three stages:crack closure(Ⅰ),stable crack growth(Ⅱ),and rapid crack propagation(Ⅲ).Fractal dimensions were computed for both singleand double-crack specimens using the Otsu method and the proposed binarization technique.The Otsu method yielded values of 1.457,1.482,1.131,1.512,1.489,1.536,1.171,and 1.491,whereas the new method produced higher values—1.6038,1.6643,1.2713,1.6806,1.5594,1.6282,1.2239,and 1.6565—indicating enhanced fractal characteristics.Furthermore,the proposed method detected a three-phase evolution in fractal dimension before failure,which follows an initial increase,a stable period,and a finalrapid rise.These findingsprovide theoretical support for the application of the proposed method in underground engineering. 展开更多
关键词 Concrete-sandstone Fractal theory Box counting method Image threshold segmentation method Digital image correlation(DIC) Acoustic emission(AE)
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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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Hybrid Ensemble and Federated Learning Framework for Privacy-Preserving Cardiovascular MRI Segmentation 认领 引用
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作者 Karim Gasmi Afrah Alanazi +4 位作者 Inam Alanazi Sahar Almenwer Norah Alanazi Sarah Almaghrabi Samia Yahyaoui 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1245-1287,共43页
Cardiac magnetic resonance imaging(MRI)segmentation is an essential aspect of quantitative cardiovascular analysis,facilitating accurate evaluation of ventricular volumes,myocardial mass,and functional parameters.Deep... Cardiac magnetic resonance imaging(MRI)segmentation is an essential aspect of quantitative cardiovascular analysis,facilitating accurate evaluation of ventricular volumes,myocardial mass,and functional parameters.Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC,but they remain challenging to deploy in real-world multi-centre settings.Data privacy laws make it hard to share data across institutions,and differences in imaging protocols and patient populationsmean that data is not always distributed in the same way(non-IID).This can have a big impact on how wellmodels work together and how well they generalise.To address these issues,we first evaluate advanced segmentation architectures,including UNet++and FPN with EfficientNet-based encoders,and assess multiple hybrid combinations at the probability level.We further improve the ensemble strategy by using a genetic algorithm to automatically identify the optimal model-weighting scheme,rather than fixed combination coefficients.The genetic algorithm explores the solution space to identify the optimal weight configuration based on segmentation metrics.The best hybrid configuration is then chosen as the input architecture for the federated learning stage.We propose a privacy-preserving federated ensemble framework that enables multiple clients to collaboratively train segmentation models without sharing raw MRI data.We methodically evaluate three federated optimisation strategies:FedAvg under IID and non-IID client distributions,and FedProx,which incorporates proximal regularisation to reduce client drift.The genetically optimised ensemble is always used in all federated setups.A thorough analysis of ACDC testing volumes employing overlap-and boundary-based metrics illustrates that the amalgamation of hybrid learning with genetic optimisation and federated training enhances robustness in heterogeneous environments while maintaining data confidentiality,thus providing an efficient approach for secure multi-centre cardiac MRI segmentation. 展开更多
关键词 SDG 3 cardiovascular imaging segmentation ensemble deep learning genetic algorithm federated learning privacy-preserving AI
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Explainable Segmentation-Guided Mamba-Transformer Framework for Automated Cardiovascular Disease Detection 认领 引用
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作者 Ghada Atteia Abdulaziz Altamimi +4 位作者 Nihal Abuzinadah Khaled Alnowaiser Muhammad Umer Yunyoung Nam Yongwon Cho 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1263-1289,共27页
Cardiovascular diseases(CVD)remain the leading cause of global mortality,making early and accurate diagnosis essential for improving patient outcomes.However,most existing deep learning approaches address cardiac imag... Cardiovascular diseases(CVD)remain the leading cause of global mortality,making early and accurate diagnosis essential for improving patient outcomes.However,most existing deep learning approaches address cardiac image segmentation or disease classification independently,limiting their effectiveness in complex clinical decisionmaking scenarios.In this study,we propose an explainable spatio-temporal deep learning framework that integrates segmentation-guided representation learning with efficient temporal modeling for automated CVD detection.The proposed architecture incorporates the Segment Anything Model for Medical Imaging in 2D(SAM-Med2D)to achieve accurate cardiac structure segmentation,followed by Mamba-based temporal feature extraction and Transformerdriven spatial representation learning to capture both dynamic motion patterns and anatomical dependencies in cardiac imaging sequences.To enhance transparency and clinical trust,Gradient-weighted Class Activation Mapping(GradCAM)and SHapley Additive exPlanations(SHAP)are employed to provide interpretable diagnostic insights.The framework is evaluated on three benchmark cardiovascular datasets,including EchoNet-Dynamic,CAMUS echocardiography,and UK Biobank cine cardiac magnetic resonance imaging(CMR).Experimental results demonstrate strong performance,achieving a Dice score of 91.20%for segmentation,an AUC of 95.50%,classification accuracy of 92.10%,and an MCC of 0.84,consistently outperforming multiple baseline methods.The proposed framework consistently outperforms baseline and existing methods,achieving approximately 3%-6%improvement in segmentation performance and 3%-4%improvement in classification accuracy across key evaluation metrics.The proposed approach offers a robust and explainable solution for automated cardiovascular disease detection,with significant potential to support reliable clinical deployment and improve diagnostic workflows in medical imaging practice. 展开更多
关键词 Medical imaging explainable artificial intelligence transformer segmentation cardiovascular disease detection
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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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FMTNet:A Fourier-Mamba–Transformer Enhanced Network for Medical Image Segmentation 认领 引用
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作者 Shaoqiang Wang Guiling Shi +5 位作者 Yuanyuan Zhang Sibo Qiao Yuchen Wang Yifan Wang Yawu Zhao Xiaochun Cheng 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期798-815,共18页
Models based on U-shaped networks have achieved widespread success in the field of medical image segmentation,but their performance is generally limited by structural bottlenecks in the network.At this stage,feature m... Models based on U-shaped networks have achieved widespread success in the field of medical image segmentation,but their performance is generally limited by structural bottlenecks in the network.At this stage,feature maps experience a sharp decline in spatial resolution due to continuous downsampling,resulting in significant loss of critical boundaries and structural details.Additionally,the local receptive fields of convolutions limit the effective modelling of global context.To address this core issue,we propose a novel enhanced segmentation network called FMTNet.FMTNet fundamentally enhances the expressive power of deep features by integrating an innovative composite enhancement module at the bottleneck of the U-Net.This module consists of three synergistically working submodules:the Fourier spatial fusion module,which introduces a frequency-domain perspective to compensate for and reconstruct high-frequency structural information lost in the spatial domain;the hybrid mamba–transformer module,which efficiently captures cross-regional long-range dependencies to establish global context and the multi-scale context Aggregation module,which fuses features of different scales to adapt to objects of varying sizes.We conducted extensive experiments on multiple public multi-modal datasets,including colonoscopy polyps,dermatoscopy lesions,breast ultrasound and dental X-rays.The results demonstrate that FMTNet comprehensively outperforms SOTA methods across all key metrics,showcasing exceptional segmentation accuracy and generalisation capabilities.Our research study demonstrates that by synergistically enhancing deep features across three dimensions—frequency,global,and multi-scale—FMTNet provides a general and efficient solution to address the bottleneck issues of U-Net,significantly enhancing the accuracy and robustness of medical image segmentation.The source code and pre-trained weights are available at http://gffzz188fe103f8f1460asvbpnbqv0v6c06xnq.ffgz.tsg.suse.edu.cn/shiguiling0-has/FMTNet. 展开更多
关键词 bottleneck enhancement Fourier transform medical image segmentation multi‐scale feature fusion
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Semi-Supervised Segmentation Framework for Quantitative Analysis of Material Microstructure Images 认领 引用
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作者 Yingli Liu Weiyong Tang +2 位作者 Xiao Yang Jiancheng Yin Haihe Zhou 《Computers, Materials & Continua》 SCIE EI 2026年第4期596-611,共16页
Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subje... Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subjective,while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data.Furthermore,existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data.To address these issues,this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation.First,we introduce a Rotational Uncertainty Correction Strategy(RUCS).This strategy employs multi-angle rotational perturbations andMonte Carlo sampling to assess prediction consistency,generating a pixel-wise confidence weight map.By integrating this map into the loss function,the model dynamically focuses on high-confidence regions,thereby improving generalization ability while reducing manual annotation pressure.Second,we design a Boundary EnhancementModule(BEM)to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions.This module guides the model to focus on the boundary regions of adjacent particles,effectively resolving particle adhesion and improving segmentation accuracy.Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset(ASAD).Results indicate that the proposed method performs exceptionally well with scarce labeled data.Specifically,using only 5%labeled data,our method improves the Jaccard index and Adjusted Rand Index(ARI)by 2.84 and 1.57 percentage points,respectively,and reduces the Variation of Information(VI)by 8.65 compared to stateof-the-art semi-supervised models,approaching the performance levels of 10%labeled data.These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs. 展开更多
关键词 Microstructure alloy semi-supervised segmentation boundary enhancement variation of information
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YOLOSpecNN:a novelγ-ray spectra full-energy peak automatic search and segmentation model inspired by YOLO 认领 引用
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作者 Cao‑Lin Zhang Jiang‑Mei Zhang +2 位作者 Hao‑Lin Liu Shu‑Ya Qin Jia‑Qi Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第7期43-55,共13页
Qualitative identification and analysis of radioactive nuclides in unknown environments are essential for the remote monitoring and prompt early warning of radioactive contamination.In recent years,deep learning techn... Qualitative identification and analysis of radioactive nuclides in unknown environments are essential for the remote monitoring and prompt early warning of radioactive contamination.In recent years,deep learning techniques have made significant strides in automated qualitative identification.However,the quantitative analysis of radioactive nuclides still depends on traditional methods to determine peak positions and boundaries.These methods often require extensive manual expertise and parameter tuning and thus fail to meet the demands of unmanned remote monitoring.This paper presents a novel framework for automatic full-energy peak segmentation,called YOLOSpecNN.We introduce a multi-root mean square error joint optimization function and a unified regression model capable of simultaneously predicting the central position,boundaries,and confidence of full-energy peaks.To address the challenge of low recall rates due to narrow,low-intensity,and overlapping peaks,we propose a new multiscale context feature extraction module(MSNN module).This module effectively enhances the local detailed features and significantly improves the recall rates.The effectiveness of the proposed method is validated using six artificial radioactive nuclides(241Am,57Co,131I,134Cs,137Cs,and 60Co)along with 40K,and a mixed-energy spectrum dataset is constructed for quantitative evaluation.The experimental results show that the proposed method significantly outperforms traditional approaches,achieving a precision of 0.998,a recall of 0.95,the best F1 score of 0.974@0.427,and an average precision of 0.946.Compared with traditional morphological methods,the proposed method improves the precision,recall,and best F1 score by 0.512,0.199,and 0.391,respectively.Ablation experiments further reveal that the MSNN module notably enhances the recall by 0.067.Moreover,the proposed method performs excellently even in challenging environments with low gross counts and a low signal-to-noise ratio(SNR),achieving state-of-the-art results.Additionally,the model achieves an average real-time inference performance of 16.1941 ms on a device with a 15-W low-power budget.Overall,the proposed method demonstrates exceptional performance in the automatic search and segmentation of full-energy peaks,offering robust support for the implementation of unmanned remote radiation monitoring systems. 展开更多
关键词 Gamma spectroscopy Gamma ray spectral analysis Peak searching and segmentation Nuclide identification
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GLMCNet: A Global-Local Multiscale Context Network for High-Resolution Remote Sensing Image Semantic Segmentation 认领 引用
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作者 Yanting Zhang Qiyue Liu +4 位作者 Chuanzhao Tian Xuewen Li Na Yang Feng Zhang Hongyue Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期2086-2110,共25页
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
关键词 Multiscale context attention mechanism remote sensing images semantic segmentation
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