Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning.Recent advances in foundation models,such as the medical-focused Segment AnythingModel(Me...Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning.Recent advances in foundation models,such as the medical-focused Segment AnythingModel(MedSAM),have demonstrated strong performance but face challenges inmanymedical applications due to anatomical complexity and a limited domain-specific prompt.Thiswork introduces amethodology that enhances segmentation robustness and precision by automatically generating multiple informative point prompts,rather than relying on single inputs.The proposed approach randomly samples sets of spatially distributed point prompts based on image features,enabling MedSAM to better capture fine-grained anatomical structures and boundaries.During inference,probability maps are aggregated to reduce local misclassifications without additional model training.Extensive experiments on various computed tomography(CT)and magnetic resonance imaging(MRI)datasets demonstrate improvements in Dice Similarity Coefficient(DSC)and Normalized Surface Dice(NSD)metrics compared to baseline SAM and Scribble Prompt models.A semi-automatic point sampling version based on the ground truth segmentations yielded enhanced results,achieving up to 92.1%DSC and 86.6%NSD,with significant gains in delineating complex organs such as the pancreas,colon,kidney,and brain tumours.The main novelty of our method consists of effectively combining the results of multiple point prompts into the medical segmentation pipeline so that single-point prompt methods are outperformed.Overall,the proposed model offers a straightforward yet effective approach to improve medical image segmentation performance while maintaining computational efficiency.展开更多
Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in ord...Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.展开更多
Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ...Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.展开更多
Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins ar...Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins are limited,especially in automation,heavily dependent on large amounts of data and resources,lacking the flexibility to adapt to different scenarios.To address these challenges,this paper introduces a novel image segmentation model,CableSAM,specifically designed for automated segmentation of cabin cables.CableSAM improves segmentation efficiency and accuracy using knowledge distillation and employs a context ensemble strategy.It accurately segments cables in various scenarios with minimal input prompts.Comparative experiments on three cable datasets demonstrate that CableSAM surpasses other advanced cable segmentation methods in performance.展开更多
This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two ke...This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two key modules:Constrained Deformable Convolution(CDC),which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets,and the Wavelet Frequency Enhancement Module(WFEM),which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures.Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance,achieving 82.39%mIoU with only 7.49M parameters and 10.34 GFLOPs,outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead.The model also shows strong cross-dataset generalization,achieving 60.01%mIoU and 66.22%F1 on Asphalt3k without fine-tuning.These results highlight CW-HRNet’s favorable accuracyefficiency trade-off for real-world crack segmentation tasks.展开更多
Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of...Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.展开更多
Microdispersion technology is crucial for a variety of applications in both the chemical and biomedical fields.The precise and rapid characterization of microdroplets and microbubbles is essential for research as well...Microdispersion technology is crucial for a variety of applications in both the chemical and biomedical fields.The precise and rapid characterization of microdroplets and microbubbles is essential for research as well as for optimizing and controlling industrial processes.Traditional methods often rely on time-consuming manual analysis.Although some deep learning-based computer vision methods have been proposed for automated identification and characterization,these approaches often rely on supervised learning,which requires labeled data for model training.This dependency on labeled data can be time-consuming and expensive,especially when working with large and complex datasets.To address these challenges,we propose Micro Flow SAM,an innovative,motion-prompted,annotation-free,and training-free instance segmentation approach.By utilizing motion of microdroplets and microbubbles as prompts,our method directs large-scale vision models to perform accurate instance segmentation without the need for annotated data or model training.This approach eliminates the need for human intervention in data labeling and reduces computational costs,significantly streamlining the data analysis process.We demonstrate the effectiveness of Micro Flow SAM across 12 diverse datasets,achieving outstanding segmentation results that are competitive with traditional methods.This novel approach not only accelerates the analysis process but also establishes a foundation for efficient process control and optimization in microfluidic applications.Micro Flow SAM represents a breakthrough in reducing the complexities and resource demands of instance segmentation,enabling faster insights and advancements in the microdispersion field.展开更多
Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion siz...Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes.To overcome these limitations,we introduce MSAMamba-UNet,a lightweight model that integrates two novel architectures:Multi-Scale Mamba(MSMamba)and Adaptive Dynamic Gating Block(ADGB).MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets.ADGB dynamically selects convolutional kernels with varying receptive fields based on input features,improving the model’s capacity to accommodate diverse lesion textures and scales.Additionally,we introduce a Mix Attention Fusion Block(MAF)to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms.Extensive evaluation of MSAMamba-UNet on the ISIC 2016,ISIC 2017,and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs.Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%,85.47%,and 82.22%,as well as DSC scores of 92.20%,92.17%,and 90.24%,respectively.These results underscore the lightweight design and effectiveness of MSAMamba-UNet.展开更多
3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
Objective:The aims of this study were to investigate the clinical applicability of 3D segmentation in measuring cochlear anatomical parameters,explore factors that influence the insertion angle of cochlear implant ele...Objective:The aims of this study were to investigate the clinical applicability of 3D segmentation in measuring cochlear anatomical parameters,explore factors that influence the insertion angle of cochlear implant electrodes in patients with inner ear malformations,and determine the value of 3D segmentation in predicting cochlear implant electrode insertion depth by simulating electrode implantation in a reconstructed 3D model.Methods:Data from 208 temporal bone CT scans of patients with a variety of inner ear malformations(including the CH,IP-Ⅰ,IP-Ⅱ,and IP-Ⅲtypes)who underwent cochlear implantation at our center were retrospectively analyzed.Preoperative temporal bone CT data were subjected to three-dimensional(3D)segmentation of the cochlea with a 3D slicer.Results:Cochlear malformation types,including IP typesⅠ(42 ears),Ⅱ(278ears),Ⅲ(20 ears),and CH(65 ears),were diagnosed and measured in 208 preoperative CT datasets.Cochlear anatomical parameters and electrode length were correlated,which partially explained the variations in electrode insertion angle.The mean angle of implantation among the enrolled patients was 564.33°,and the mean implantation angle prediction error in the 3D segmentation was|23.74|°.Conclusion:Three-dimensional segmentation from temporal bone CT is valuable for surgeons,especially in treating patients with inner ear malformation.Such insights will help surgeons understand overall anatomical variations,predict electrode implantation depth,and complete preoperative imaging assessments for cochlear implant insertion depth in patients with inner ear malformations.展开更多
Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for ...Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.展开更多
Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the...Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.展开更多
基金supported by the Autonomous Government of Andalusia(Spain)under project UMA20-FEDERJA-108also by the Ministry of Science and Innovation of Spain,grant number PID2022-136764OA-I00+1 种基金It includes funds fromthe European Regional Development Fund(ERDF),It is also partially supported by the Fundación Unicaja(PUNI-003_2023)the Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND(ATECH-25-02).
文摘Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning.Recent advances in foundation models,such as the medical-focused Segment AnythingModel(MedSAM),have demonstrated strong performance but face challenges inmanymedical applications due to anatomical complexity and a limited domain-specific prompt.Thiswork introduces amethodology that enhances segmentation robustness and precision by automatically generating multiple informative point prompts,rather than relying on single inputs.The proposed approach randomly samples sets of spatially distributed point prompts based on image features,enabling MedSAM to better capture fine-grained anatomical structures and boundaries.During inference,probability maps are aggregated to reduce local misclassifications without additional model training.Extensive experiments on various computed tomography(CT)and magnetic resonance imaging(MRI)datasets demonstrate improvements in Dice Similarity Coefficient(DSC)and Normalized Surface Dice(NSD)metrics compared to baseline SAM and Scribble Prompt models.A semi-automatic point sampling version based on the ground truth segmentations yielded enhanced results,achieving up to 92.1%DSC and 86.6%NSD,with significant gains in delineating complex organs such as the pancreas,colon,kidney,and brain tumours.The main novelty of our method consists of effectively combining the results of multiple point prompts into the medical segmentation pipeline so that single-point prompt methods are outperformed.Overall,the proposed model offers a straightforward yet effective approach to improve medical image segmentation performance while maintaining computational efficiency.
基金Natural Science Foundation of Zhejiang Province,Grant/Award Number:LY23F020025Science and Technology Commissioner Program of Huzhou,Grant/Award Number:2023GZ42Sichuan Provincial Science and Technology Support Program,Grant/Award Numbers:2023ZHCG0005,2023ZHCG0008。
文摘Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.
基金supported by Natural Science Foundation Programme of Gansu Province(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Science and Technology Plan Key Research and Development Program Project(No.24YFFA024).
文摘Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.
基金supported by the Innovation Foundation of National Commercial Aircraft Manufacturing Engineering Technology Research Center(No.COMAC-SFGS-2022-1877)in part by the National Natural Science Foundation of China(No.92048301)。
文摘Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins are limited,especially in automation,heavily dependent on large amounts of data and resources,lacking the flexibility to adapt to different scenarios.To address these challenges,this paper introduces a novel image segmentation model,CableSAM,specifically designed for automated segmentation of cabin cables.CableSAM improves segmentation efficiency and accuracy using knowledge distillation and employs a context ensemble strategy.It accurately segments cables in various scenarios with minimal input prompts.Comparative experiments on three cable datasets demonstrate that CableSAM surpasses other advanced cable segmentation methods in performance.
文摘This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two key modules:Constrained Deformable Convolution(CDC),which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets,and the Wavelet Frequency Enhancement Module(WFEM),which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures.Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance,achieving 82.39%mIoU with only 7.49M parameters and 10.34 GFLOPs,outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead.The model also shows strong cross-dataset generalization,achieving 60.01%mIoU and 66.22%F1 on Asphalt3k without fine-tuning.These results highlight CW-HRNet’s favorable accuracyefficiency trade-off for real-world crack segmentation tasks.
基金supported by the National Key R&D Program of China(No.2022YFC2504403)the National Natural Science Foundation of China(No.62172202)+1 种基金the Experiment Project of China Manned Space Program(No.HYZHXM01019)the Fundamental Research Funds for the Central Universities from Southeast University(No.3207032101C3)。
文摘Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.
基金the financial support from National Natural Science Foundation of China (21991104)。
文摘Microdispersion technology is crucial for a variety of applications in both the chemical and biomedical fields.The precise and rapid characterization of microdroplets and microbubbles is essential for research as well as for optimizing and controlling industrial processes.Traditional methods often rely on time-consuming manual analysis.Although some deep learning-based computer vision methods have been proposed for automated identification and characterization,these approaches often rely on supervised learning,which requires labeled data for model training.This dependency on labeled data can be time-consuming and expensive,especially when working with large and complex datasets.To address these challenges,we propose Micro Flow SAM,an innovative,motion-prompted,annotation-free,and training-free instance segmentation approach.By utilizing motion of microdroplets and microbubbles as prompts,our method directs large-scale vision models to perform accurate instance segmentation without the need for annotated data or model training.This approach eliminates the need for human intervention in data labeling and reduces computational costs,significantly streamlining the data analysis process.We demonstrate the effectiveness of Micro Flow SAM across 12 diverse datasets,achieving outstanding segmentation results that are competitive with traditional methods.This novel approach not only accelerates the analysis process but also establishes a foundation for efficient process control and optimization in microfluidic applications.Micro Flow SAM represents a breakthrough in reducing the complexities and resource demands of instance segmentation,enabling faster insights and advancements in the microdispersion field.
基金supported in part by the National Natural Science Foundation of China under Grant 62201201the Foundation of Henan Educational Committee under Grant 242102211042.
文摘Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes.To overcome these limitations,we introduce MSAMamba-UNet,a lightweight model that integrates two novel architectures:Multi-Scale Mamba(MSMamba)and Adaptive Dynamic Gating Block(ADGB).MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets.ADGB dynamically selects convolutional kernels with varying receptive fields based on input features,improving the model’s capacity to accommodate diverse lesion textures and scales.Additionally,we introduce a Mix Attention Fusion Block(MAF)to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms.Extensive evaluation of MSAMamba-UNet on the ISIC 2016,ISIC 2017,and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs.Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%,85.47%,and 82.22%,as well as DSC scores of 92.20%,92.17%,and 90.24%,respectively.These results underscore the lightweight design and effectiveness of MSAMamba-UNet.
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
基金supported by the National Key Research and Development Program of China(grant no.2022YFC2402705)National Municipal Natural Science Foundation(grant no.82471161)Beijing Municipal Natural Science Foundation(grant no.7244308)。
文摘Objective:The aims of this study were to investigate the clinical applicability of 3D segmentation in measuring cochlear anatomical parameters,explore factors that influence the insertion angle of cochlear implant electrodes in patients with inner ear malformations,and determine the value of 3D segmentation in predicting cochlear implant electrode insertion depth by simulating electrode implantation in a reconstructed 3D model.Methods:Data from 208 temporal bone CT scans of patients with a variety of inner ear malformations(including the CH,IP-Ⅰ,IP-Ⅱ,and IP-Ⅲtypes)who underwent cochlear implantation at our center were retrospectively analyzed.Preoperative temporal bone CT data were subjected to three-dimensional(3D)segmentation of the cochlea with a 3D slicer.Results:Cochlear malformation types,including IP typesⅠ(42 ears),Ⅱ(278ears),Ⅲ(20 ears),and CH(65 ears),were diagnosed and measured in 208 preoperative CT datasets.Cochlear anatomical parameters and electrode length were correlated,which partially explained the variations in electrode insertion angle.The mean angle of implantation among the enrolled patients was 564.33°,and the mean implantation angle prediction error in the 3D segmentation was|23.74|°.Conclusion:Three-dimensional segmentation from temporal bone CT is valuable for surgeons,especially in treating patients with inner ear malformation.Such insights will help surgeons understand overall anatomical variations,predict electrode implantation depth,and complete preoperative imaging assessments for cochlear implant insertion depth in patients with inner ear malformations.
基金supported by European Union’s Horizon Europe research and innovation programme,project AGILEHAND(Smart Grading,Handling and Packaging Solutions for Soft and Deformable Products in Agile and Reconfigurable Lines)(101092043).
文摘Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.
文摘Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.
