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Multi-Scale Image Segmentation Model for Fine-Grained Recognition of Zanthoxylum Rust 认领 引用 被引量：1
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作者 Fan Yang Jie Xu +5 位作者 Haoliang Wei Meng Ye Mingzhu Xu Qiuru Fu Lingfei Ren Zhengwen Huang 《Computers, Materials & Continua》 SCIE EI 2022年第5期2963-2980,共18页
Zanthoxylum bungeanum Maxim,generally called prickly ash,is widely grown in China.Zanthoxylum rust is the main disease affecting the growth and quality of Zanthoxylum.Traditional method for recognizing the degree of i... Zanthoxylum bungeanum Maxim,generally called prickly ash,is widely grown in China.Zanthoxylum rust is the main disease affecting the growth and quality of Zanthoxylum.Traditional method for recognizing the degree of infection of Zanthoxylum rust mainly rely on manual experience.Due to the complex colors and shapes of rust areas,the accuracy of manual recognition is low and difficult to be quantified.In recent years,the application of artificial intelligence technology in the agricultural field has gradually increased.In this paper,based on the DeepLabV2 model,we proposed a Zanthoxylum rust image segmentation model based on the FASPP module and enhanced features of rust areas.This paper constructed a fine-grained Zanthoxylum rust image dataset.In this dataset,the Zanthoxylum rust image was segmented and labeled according to leaves,spore piles,and brown lesions.The experimental results showed that the Zanthoxylum rust image segmentation method proposed in this paper was effective.The segmentation accuracy rates of leaves,spore piles and brown lesions reached 99.66%,85.16%and 82.47%respectively.MPA reached 91.80%,and MIoU reached 84.99%.At the same time,the proposed image segmentation model also had good efficiency,which can process 22 images per minute.This article provides an intelligent method for efficiently and accurately recognizing the degree of infection of Zanthoxylum rust. 展开更多
关键词 Zanthoxylum rust image segmentation deep learning
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TriLVM-UNet:Multi-Scale State Space Modeling with Cross-Channel Fusion Attention Mechanism for Precise Medical Image Segmentation 认领 引用
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作者 Kexin Zhang Lihua Liu +3 位作者 Yuting Xue Tao Zhou Fengshuai Yue Ruifeng Du 《Computers, Materials & Continua》 SCIE EI 2026年第9期2223-2252,共30页
Traditional Mamba-UNet integrations employ four-stage architectures,replacing conventional five-stage UNets with VMamba blocks for global dependency modeling.Unlike Transformers,which suffer from quadratic complexity ... Traditional Mamba-UNet integrations employ four-stage architectures,replacing conventional five-stage UNets with VMamba blocks for global dependency modeling.Unlike Transformers,which suffer from quadratic complexity and high memory consumption in self-attention,Mamba-UNet achieves efficient global modeling through linear-complexity state space modeling.This paper proposes TriLVM-UNet,a lightweight three-stage architecture that integrates parameter-efficient VMamba blocks and enhances cross-stage feature interaction via an improved skip-attention bridge(SAB)module inspired by UltraLight VM-UNet.The model incorporates a Lightweight Vision Mamba(LVM)layer for high-resolution feature extraction,alongside multi-scale dilated convolution(MSDC)and convolutional block attention module(CBAM)for enhanced feature fusion.Evaluated on the 3D ACDC dataset against six baseline models,TriLVM-UNet achieves 98.57%accuracy.The GitHub repository is available at:http://gffzz188fe103f8f1460asbc55wqwfwqno6ovq.ffgz.tsg.suse.edu.cn/730432ch/TriLVM-UNet. 展开更多
关键词 Medical image segmentation visual state space model TriLVM-UNet lightweight architecture
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M2ANet:Multi-branch and multi-scale attention network for medical image segmentation 认领 引用 被引量：1
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作者 Wei Xue Chuanghui Chen +3 位作者 Xuan Qi Jian Qin Zhen Tang Yongsheng He 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第8期547-559,共13页
Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ... Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures. 展开更多
关键词 medical image segmentation convolutional neural network multi-branch attention multi-scale feature fusion
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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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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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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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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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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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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://gffzz188fe103f8f1460asbc55wqwfwqno6ovq.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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M2ATNet: Multi-Scale Multi-Attention Denoising and Feature Fusion Transformer for Low-Light Image Enhancement 认领 引用
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作者 Zhongliang Wei Jianlong An Chang Su 《Computers, Materials & Continua》 SCIE EI 2026年第1期1819-1838,共20页
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach... Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments. 展开更多
关键词 Low-light image enhancement multi-scale multi-attention transformer
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An APO Algorithm Based on Taguchi Methods and Its Application in Multi-Level Image Segmentation 认领 引用
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作者 Jeng-Shyang Pan Yan-Na Wei +3 位作者 Ling-Da Chi Shu-Chuan Chu Ru-Yu Wang Junzo Watada 《Computers, Materials & Continua》 SCIE EI 2026年第5期814-837,共24页
Multilevel image segmentation is a critical task in image analysis,which imposes high requirements on the global search capability and convergence efficiency of segmentation algorithms.In this paper,an improved Artifi... Multilevel image segmentation is a critical task in image analysis,which imposes high requirements on the global search capability and convergence efficiency of segmentation algorithms.In this paper,an improved Artificial Protozoa Optimization algorithm,termed the two-stage Taguchi-assisted Gaussian–Levy Artificial Protozoa Optimization(TGAPO)algorithm,is proposed and applied tomultilevel image segmentation.The proposed algorithm adopts a two-stage evolutionary mechanism.In the first stage,Gaussian perturbation is introduced to enhance local search capability;in the second stage,Levy flight is incorporated to expand the global search range;and finally,the Taguchi strategy is employed to further refine the optimal solution.Consequently,the global optimization performance and robustness of the algorithm are significantly improved.To evaluate the effectiveness of the proposed TGAPO algorithm,comparative experiments are conducted with representative optimization algorithms,including the Grey Wolf Optimizer(GWO)and Particle Swarm Optimization(PSO),in the context ofmultilevel image segmentation.The segmentation quality is assessed using the minimum cross-entropy function as the performance metric.Experimental results demonstrate that the TGAPO algorithm outperforms the comparison algorithms in terms of segmentation accuracy and convergence speed,and exhibits superior stability in high-threshold segmentation tasks.Furthermore,the proposedmethod achieves excellentmulti-threshold segmentation performance for color images and shows strong potential for practical applications. 展开更多
关键词 Meta-heuristic algorithm multilevel image segmentation taguchi strategy minimum cross-entropy threshold artificial protozoa optimization(APO)
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GLF-Segformer:an improved Segformer model integrating local and global information for skin cancer image segmentation 认领 引用
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作者 Xiangyu DENG Yapeng ZHENG 《Optoelectronics Letters》 EI 2026年第4期236-242,共7页
More accurate segmentation of skin cancers in dermoscopy images is crucial for clinical treatment.However,the prevalence of interfering noise in dermoscopy images poses a challenge to its accurate segmentation.For thi... More accurate segmentation of skin cancers in dermoscopy images is crucial for clinical treatment.However,the prevalence of interfering noise in dermoscopy images poses a challenge to its accurate segmentation.For this reason,this paper proposes an improved GLF-Segformer to improve segmentation.The model adds polarized self-attention(PSA)module and R-convolution and attention fusion module(R-CAFM)to the Segformer’s encoder to enhance the ability to capture local information and facilitate the effective fusion of local and global information.The decoder employs an innovative two-stage hybrid up-sampling to effectively reduce information loss.In addition,a new hybrid loss function is designed to further improve the segmentation accuracy of the model at complex boundaries.The experimental results show that GLF-Segformer achieves 90.73%and 89.85%mean intersection over union(mIoU)on two standard datasets,ISIC2017 and ISIC2018,respectively,and exhibits better segmentation performance compared to other comparison algorithms. 展开更多
关键词 capture local information dermoscopy images interfering noise clinical treatmenthoweverthe segmentation global local information skin cancers
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PointNMSA: An Improved PointNeXt Network with Non-Local Multi-Scale Aggregation for 3D Point Cloud Semantic Segmentation 认领 引用
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作者 Aihua Wu Chenlu Huang 《Computers, Materials & Continua》 SCIE EI 2026年第8期1632-1649,共18页
Three-dimensional(3D)point cloud semantic segmentation is a core task in indoor scene understanding,providing detailed semantic information about spatial structures and object categories in indoor environments.Althoug... Three-dimensional(3D)point cloud semantic segmentation is a core task in indoor scene understanding,providing detailed semantic information about spatial structures and object categories in indoor environments.Although methods based on deep learning have made steady progress in recent years,accurately segmenting complex indoor scenes remains challenging due to the unordered nature of point clouds and variations across large scales.Most existing networks have limited capability for multi-scale feature aggregation and struggle to balance local geometric details with global semantic context.These issues are further exacerbated by hierarchical downsampling,which often leads to the loss of fine-grained structural information.Moreover,feature interaction restricted to local neighborhoods may limit the capture of non-local semantic dependencies in complex indoor scenes.To address these limitations,we propose PointNMSA(PointNeXt with Non-local Multi-Scale Aggregation),an improved semantic segmentation network built upon the PointNeXt backbone.A Multi-Scale Feature Enhancement(MSFE)module is introduced in the decoding stage to fuse features from different encoding levels,and further refines the fused features to produce more stable multi-scale representations,which preserves geometric details across scales.In addition,a Convolution-Attention Mixing(CA-Mix)module is designed to jointly integrate local spatial structures and non-local contextual dependencies via dual-stream aggregation and multi-dimensional attention fusion,thereby enabling more discriminative feature representations.Experiments on the Stanford Large-Scale 3D Indoor Spaces(S3DIS)benchmark demonstrate the effectiveness of PointNMSA.On the Area 5 test split,PointNMSA achieves a mean intersection over union(mIoU)of 65.10%,outperforming the PointNeXt baseline by 1.59%,while introducing only a modest increase in computational cost(latency from 42.24 to 45.18 ms and parameters from 3.16 to 8.67M).Despite the noticeable growth in parameter count,the increase in inference latency remains relatively limited,indicating a favorable trade-off between segmentation accuracy and computational efficiency.Additional cross-dataset experiments on ScanNet further verify that PointNMSA maintains stable gains under different indoor scene distributions.Such performance gains suggest that PointNMSA provides a more robust and generalizable solution for semantic segmentation in large-scale indoor environments with complex structural layouts. 展开更多
关键词 3D point cloud semantic segmentation indoor scene understanding multi-scale feature aggregation non-local context integration PointNeXt
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High Resolution Remote Sensing Image Segmentation Method with Improved DeepLabv3+ 认领 引用 被引量：2
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作者 TAO Hongjie LI Zhaofei +2 位作者 QI Fei CHEN Jingjue ZHOU Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期348-358,共11页
In order to address the challenges associated with poor semantic segmentation results of classical semantic segmentation networks in high-resolution remote sensing images,limited performance in complex scenes,a large ... In order to address the challenges associated with poor semantic segmentation results of classical semantic segmentation networks in high-resolution remote sensing images,limited performance in complex scenes,a large number of network parameters,and high training costs,this study proposes an efficient segmentation method for high-resolution remote sensing images based on an improved DeepLabv3+approach.The method focuses on three key aspects:reducing the number of network parameters,minimizing computation volume,and enhancing performance.First,the proposed method replaces the original DeepLabv3+backbone network Xception,which is computationally heavy,with the lighter MobileNetV2 network for feature extraction.This substitution helps reduce the number of network parameters while maintaining effective feature extraction.Second,a lightweight convolutional block attention module(CBAM)is added after the feature extraction module to enhance the network’s feature extraction capability.The inclusion of CBAM further reduces the number of network parameters.Last,coordinate attention is introduced after the shallow features obtained from the feature extraction module.This addition allows the network to focus more on relevant features in the image,while disregarding irrelevant background information.Experimental results demonstrate the effectiveness of the proposed method.In the segmentation task of the high-resolution image dataset,the method achieves a mean intersection over union(mIoU)of 75.33%.This result surpasses mainstream semantic segmentation networks such as SegNet,PSPNet,and U-Net by 12.49%,3.16%,and 1.62%respectively.Furthermore,the proposed model has a relatively low number of network parameters,with only 6.02×106 parameters,and a computation volume of 26.45 GFLOPs.This balance between computational efficiency and segmentation accuracy makes the model highly valuable for edge computing applications. 展开更多
关键词 remote sensing image DeepLabv3+ MobileNetV2 attention mechanism semantic segmentation
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Advances in deep learning for bacterial image segmentation in optical microscopy 认领 引用
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作者 Zhijun Tan Yang Ding +6 位作者 Huibin Ma Jintao Li Danrou Zheng Hua Bai Weini Xin Lin Li Bo Peng 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第1期30-44,共15页
Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bac... Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bacterial structures,facilitating precise measurement of morphological variations and population behaviors at single-cell resolution.This paper reviews advancements in bacterial image segmentation,emphasizing the shift from traditional thresholding and watershed methods to deep learning-driven approaches.Convolutional neural networks(CNNs),U-Net architectures,and three-dimensional(3D)frameworks excel at segmenting dense biofilms and resolving antibiotic-induced morphological changes.These methods combine automated feature extraction with physics-informed postprocessing.Despite progress,challenges persist in computational efficiency,cross-species generalizability,and integration with multimodal experimental workflows.Future progress will depend on improving model robustness across species and imaging modalities,integrating multimodal data for phenotype-function mapping,and developing standard pipelines that link computational tools with clinical diagnostics.These innovations will expand microbial phenotyping beyond structural analysis,enabling deeper insights into bacterial physiology and ecological interactions. 展开更多
关键词 Bacterial image deep learning optical microscopy image segmentation artificial intelligence
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C-SegNet:a practical approach for automated diabetic macular edema segmentation in optical coherence tomography images 认领 引用
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作者 Zhi-Yuan Guan Ge Deng +6 位作者 Shi-Long Shi Zhen Tang Xian-Kun Dong Qiu-Yi Li Shu-Jing Shen Yong-Ling He Xue-Jun Qiu 《Biomedical Engineering Communications》 CAS 2026年第2期15-22,共8页
Background:Diabetic macular edema is a prevalent retinal condition and a leading cause of visual impairment among diabetic patients’Early detection of affected areas is beneficial for effective diagnosis and treatmen... Background:Diabetic macular edema is a prevalent retinal condition and a leading cause of visual impairment among diabetic patients’Early detection of affected areas is beneficial for effective diagnosis and treatment.Traditionally,diagnosis relies on optical coherence tomography imaging technology interpreted by ophthalmologists.However,this manual image interpretation is often slow and subjective.Therefore,developing automated segmentation for macular edema images is essential to enhance to improve the diagnosis efficiency and accuracy.Methods:In order to improve clinical diagnostic efficiency and accuracy,we proposed a SegNet network structure integrated with a convolutional block attention module(CBAM).This network introduces a multi-scale input module,the CBAM attention mechanism,and jump connection.The multi-scale input module enhances the network’s perceptual capabilities,while the lightweight CBAM effectively fuses relevant features across channels and spatial dimensions,allowing for better learning of varying information levels.Results:Experimental results demonstrate that the proposed network achieves an IoU of 80.127%and an accuracy of 99.162%.Compared to the traditional segmentation network,this model has fewer parameters,faster training and testing speed,and superior performance on semantic segmentation tasks,indicating its highly practical applicability.Conclusion:The C-SegNet proposed in this study enables accurate segmentation of Diabetic macular edema lesion images,which facilitates quicker diagnosis for healthcare professionals. 展开更多
关键词 multi-scale input diabetic macular edema image segmentation optical coherence tomography
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A medical image segmentation model based on SAM with an integrated local multi-scale feature encoder 认领 引用 被引量：1
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作者 DI Jing ZHU Yunlong LIANG Chan 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2025年第3期359-370,共12页
Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ... Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis. 展开更多
关键词 segment anything model(SAM) medical image segmentation encoder decoder multiaxial Hadamard product module(MHPM) cross-branch balancing adapter
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RE-UKAN:A Medical Image Segmentation Network Based on Residual Network and Efficient Local Attention 认领 引用
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作者 Bo Li Jie Jia +2 位作者 Peiwen Tan Xinyan Chen Dongjin Li 《Computers, Materials & Continua》 SCIE EI 2026年第3期2184-2200,共17页
Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual infor... Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation. 展开更多
关键词 Image segmentation U-KAN residual network ELA
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Learning Conceptual Text Prompts from Visual Regions of Interest for Medical Image Segmentation 认领 引用
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作者 Zhu He Haoran Zhang +4 位作者 Wentao Zhang Shen Zhao Qiqi Liu Xiaohu Wu Qicheng Lao 《Engineering》 SCIE EI CSCD 2026年第7期198-213,共16页
Vision-language segmentation models (VLSMs) are effective in medical image segmentation tasks. However, a major limitation of these models is their dependence on manually crafted textual inputs. Studies have used visu... Vision-language segmentation models (VLSMs) are effective in medical image segmentation tasks. However, a major limitation of these models is their dependence on manually crafted textual inputs. Studies have used visual question answering to semiautomatically generate textual information. However, these methods encounter challenges such as error accumulation. Herein, we propose a method to learn conceptual text prompts directly from visual regions of interest (ROIs) for facilitating medical image segmentation. We extracted textual conceptual attributes from ROIs using a large multimodal model to derive coarse real-text prompts. A text latent space transformation module accepted the ROI images as input for generating fine-grained pseudo-text prompts to compensate for the lack of image detail perception in the abovementioned real-text prompts. These prompts were encoded into a unified text embedding. Thereafter, we applied a self-adding noise knowledge distillation method to transfer the knowledge from text embedding to the class token of the image encoder, enabling direct text-guided inference during testing while reducing error accumulation. Our approach minimized the need for manual prompt design by leveraging explicit discrete and implicit continuous text prompts to effectively guide visual segmentation. Extensive evaluation across 13 medical image segmentation datasets demonstrated that our model outperformed the state-of-the-art VLSMs and vision-based segmentation models, exhibiting superior segmentation accuracy. 展开更多
关键词 Conceptual text Prompt learning Knowledge distillation Medical image segmentation
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DenT:Dense-Transformer for Label-Free Microscopy Image Segmentation 认领 引用
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作者 Chan-Min Hsu Shang-Ru Yang +1 位作者 Yi-Ju Lee An-Chi Wei 《Computers, Materials & Continua》 SCIE EI 2026年第7期278-293,共16页
U-Net,a fully convolutional neural network(FCNN)with U-shaped features,has demonstrated significant success in biomedical image segmentation.However,the locality of convolution operations in the U-Net limits its abili... U-Net,a fully convolutional neural network(FCNN)with U-shaped features,has demonstrated significant success in biomedical image segmentation.However,the locality of convolution operations in the U-Net limits its ability to learn long-range dependencies.Transformers,originally developed for natural language processing,have recently been adapted for image segmentation because of their global self-attention mechanisms.Inspired by the long-range feature learning capability of transformers,we propose Dense-Transformer(DenT),an architecture designed for volumetric microscopy image segmentation.DenT incorporates transformers as encoders within each convolutional layer to capture global contextual information.Additionally,dense skip connections at multiple resolutions enhance feature propagation,enabling precise localization.We evaluated DenT on mitochondrial segmentation using our confocal microscopy dataset and a public fluorescence microscope dataset from the Allen Institute for Cell Science.The experimental results demonstrate that DenT incrementally improves the segmentation of mitochondria and mitochondrial DNA substructures from transmitted light microscopy images.DenT offers a tool for visualization,measurement,and analysis of mitochondrial morphology and mitochondrial DNA in label-free microscopy. 展开更多
关键词 UNet transformer image segmentation mitochondrial organelle deep learning microscopy imaging
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