Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting v...Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting various illnesses automatically. What has been highlighted among the existing exams that allow diagnosis aid and the application of computing systems in parallel is Computed Tomography (CT). CT enables the visualization of internal organs, such as the lung and its structures. Computational Vision systems extract information from the CT images by segmenting the regions of interest, and then recognize and identify details in those images. This work focuses on the segmentation phase of CT lung images with singularity-based techniques. Among these methods are the region growing (RG) technique and its 3D RG variations and the thresholding technique with multi-thresholding. The 3D RG method is applied to lung segmentation and from the 3D RG segments of the lung hilum, the multi-thresholding can segment the blood vessels, lung emphysema and the bones. The results of lung segmentation in this work were evaluated by two pulmonologists. The results obtained showed that these methods can integrate aid systems for medical diagnosis in the pulmonology field.展开更多
The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the probl...The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.展开更多
As a branch of computer science,information visualization aims to help users understand and analyze complex data through graphical interfaces and interactive technologies.Information visualization primarily includes v...As a branch of computer science,information visualization aims to help users understand and analyze complex data through graphical interfaces and interactive technologies.Information visualization primarily includes various visual structures such as time-series structures,spatial relationship structures,statistical distribution structures,and geographic map structures,each with unique functions and application scenarios.To better explain the cognitive process of visualization,researchers have proposed various cognitive models based on interaction mechanisms,visual perception steps,and novice use of visualization.These models help understand user cognition in information visualization,enhancing the effectiveness of data analysis and decision-making.展开更多
Three-dimensional structured illumination microscopy(3DSIM)is an essential super-resolution imaging technique for visualizing volumetric subcellular structures at the nanoscale,capable of doubling both lateral and axi...Three-dimensional structured illumination microscopy(3DSIM)is an essential super-resolution imaging technique for visualizing volumetric subcellular structures at the nanoscale,capable of doubling both lateral and axial resolution beyond the diffraction limit.However,high-quality 3DSIM reconstruction is often hindered by uncertainties in experimental parameters,such as optical aberrations and fluorescence density heterogeneity.Here,we present PCA-3DSIM,a novel 3DSIM reconstruction framework that extends principal component analysis(PCA)from two-dimensional(2D)to three-dimensional(3D)super-resolution microscopy.To further compensate spatial nonuniformities of illumination parameters,PCA-3DSIM can be implemented in an adaptive tiled-block manner.By segmenting raw volumetric data into localized subsets,PCA-3DSIM enables accurate parameter estimation and effective interference rejection for high-fidelity,artifact-free 3D super-resolution reconstruction,with the inherent efficiency of PCA supporting the tiled reconstruction with limited computational burden.Experimental results demonstrate that PCA-3DSIM provides reliable reconstruction performance and improved robustness across diverse imaging scenarios,from custom-built platforms to commercial systems.These results establish PCA-3DSIM as a flexible and practical tool for super-resolved volumetric imaging of subcellular structures,with broad potential applications in biomedical research.展开更多
Linking similar proteins structurally is a challenging task that may help in finding the novel members of a protein family. In this respect, identification of conserved sequence can facilitate understanding and classi...Linking similar proteins structurally is a challenging task that may help in finding the novel members of a protein family. In this respect, identification of conserved sequence can facilitate understanding and classifying the exact role of proteins. However, the exact role of these conserved elements cannot be elucidated without structural and physiochemical information. In this work, we present a novel desktop application MotViz designed for searching and analyzing the conserved sequence segments within protein structure. With MotViz, the user can extract a complete list of sequence motifs from loaded 3D structures, annotate the motifs structurally and analyze their physiochemical properties. The conservation value calculated for an individual motif can be visualized graphically. To check the efficiency, predicted motifs from the data sets of 9 protein families were analyzed and Mot^z algorithm was more efficient in comparison to other online motif prediction tools. Furthermore, a database was also integrated for storing, retrieving and performing the detailed functional annotation studies. In summary, MotViz effectively predicts motifs with high sensitivity and simultaneously visualizes them into 3D strucures. Moreover, Mot- V/z is user-friendly with optimized graphical parameters and better processing speed due to the inclusion of a database at the back end. MotViz is available at http：//www.fi-pk.corn/motviz.html.展开更多
A major challenge in neuroscience is visualizing the structure of the human brain at different scales.Traditional histology reveals micro-and meso-scale brain features but suffers from staining variability,tissue dama...A major challenge in neuroscience is visualizing the structure of the human brain at different scales.Traditional histology reveals micro-and meso-scale brain features but suffers from staining variability,tissue damage,and distortion,which impedes accurate 3D reconstructions.The emerging label-free serial sectioning optical coherence tomography(S-OCT)technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features.Here,we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining(DS)model.This enhanced imaging modality integrates high-throughput 3D imaging,low sample variability and high interpretability,making it suitable for 3D histology studies.We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver staining.We demonstrate DS on various human cerebral cortex samples,achieving consistent staining quality and enhancing contrast across cortical layer boundaries.Additionally,we show that Ds preserves geometry in 3D on cubic-centimeter tissue blocks,allowing for visualization of meso-scale vessel networks in the white matter.We believe that our technique has the potential for high-throughput,multiscale imaging of brain tissues and may facilitate studies of brain structures.展开更多
Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets...Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets like chromatin.Chromatin exhibits a hierarchical organization with structural compartments and domains at different length scales,from nanometers to micrometers.Single molecule localization microscopy(SMLM)methods,such as STORM,are essential for studying chromatin at the supra-nucleosome level due to their ability to target epigenetic marks that determine chromatin organization.Multi-label imaging of chromatin is necessary to unpack its structural complexity.However,these efforts are challenged by the high-density nuclear environment,which can affect antibody binding affinities,diffusivity and non-specific interactions.Optimizing buffer conditions,fluorophore stability,and antibody specificity is crucial for achieving effective antibody conjugates.Here,we demonstrate a sequential immunolabeling protocol that reliably enables three-color studies within the dense nuclear environment.This protocol couples multiplexed localization datasets with a robust analysis algorithm,which utilizes localizations from one target as seed points for distance,density and multi-label joint affinity measurements to explore complex organization of all three targets.Applying this multiplexed algorithm to analyze distance and joint density reveals that heterochromatin and euchromatin are not-distinct territories,but that localization of transcription and euchromatin couple with the periphery of heterochromatic clusters.This work is a crucial step in molecular imaging of the dense nuclear environment as multi-label capacity enables for investigation of complex multi-component systems like chromatin with enhanced accuracy.展开更多
Biological imaging plays a pivotal role in visualizing and understanding biological structures and processes from molecular to macroscopic levels,enabling clinicians and researchers to noninvasively observe internal a...Biological imaging plays a pivotal role in visualizing and understanding biological structures and processes from molecular to macroscopic levels,enabling clinicians and researchers to noninvasively observe internal anatomy,detect disease at early stages,plan treatments,and monitor therapeutic outcomes.This broad field encompasses a diverse array of imaging modalities such as fluorescence,magnetic resonance imaging(MRI),and positron emission tomography(PET),each relying on distinct physical principles to extract specific biological information.Among them,Raman imaging has emerged as a molecularly specific and powerful technique capable of providing detailed chemical information and generating high-resolution two-or three-dimensional maps that visualize the multiscale distribution of specific molecular components within a sample.展开更多
文摘Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting various illnesses automatically. What has been highlighted among the existing exams that allow diagnosis aid and the application of computing systems in parallel is Computed Tomography (CT). CT enables the visualization of internal organs, such as the lung and its structures. Computational Vision systems extract information from the CT images by segmenting the regions of interest, and then recognize and identify details in those images. This work focuses on the segmentation phase of CT lung images with singularity-based techniques. Among these methods are the region growing (RG) technique and its 3D RG variations and the thresholding technique with multi-thresholding. The 3D RG method is applied to lung segmentation and from the 3D RG segments of the lung hilum, the multi-thresholding can segment the blood vessels, lung emphysema and the bones. The results of lung segmentation in this work were evaluated by two pulmonologists. The results obtained showed that these methods can integrate aid systems for medical diagnosis in the pulmonology field.
基金Supported by the Special Fund for Basic Scientific Research of Central-Level Public Welfare Scientific Research Institutes(2024-9007)。
文摘The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.
文摘As a branch of computer science,information visualization aims to help users understand and analyze complex data through graphical interfaces and interactive technologies.Information visualization primarily includes various visual structures such as time-series structures,spatial relationship structures,statistical distribution structures,and geographic map structures,each with unique functions and application scenarios.To better explain the cognitive process of visualization,researchers have proposed various cognitive models based on interaction mechanisms,visual perception steps,and novice use of visualization.These models help understand user cognition in information visualization,enhancing the effectiveness of data analysis and decision-making.
基金supported by National Natural Science Foundation of China(62405136,62275125,62275121,12204239,62175109)China Postdoctoral Science Foundation(BX20240486,2024M754141)+3 种基金Youth Foundation of Jiangsu Province(BK20241466,BK20220946)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB671)Fundamental Research Funds for the Central Universities(30925010309,30922010313,2023102001)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGPCXZNGZ202402,JSGP202201).
文摘Three-dimensional structured illumination microscopy(3DSIM)is an essential super-resolution imaging technique for visualizing volumetric subcellular structures at the nanoscale,capable of doubling both lateral and axial resolution beyond the diffraction limit.However,high-quality 3DSIM reconstruction is often hindered by uncertainties in experimental parameters,such as optical aberrations and fluorescence density heterogeneity.Here,we present PCA-3DSIM,a novel 3DSIM reconstruction framework that extends principal component analysis(PCA)from two-dimensional(2D)to three-dimensional(3D)super-resolution microscopy.To further compensate spatial nonuniformities of illumination parameters,PCA-3DSIM can be implemented in an adaptive tiled-block manner.By segmenting raw volumetric data into localized subsets,PCA-3DSIM enables accurate parameter estimation and effective interference rejection for high-fidelity,artifact-free 3D super-resolution reconstruction,with the inherent efficiency of PCA supporting the tiled reconstruction with limited computational burden.Experimental results demonstrate that PCA-3DSIM provides reliable reconstruction performance and improved robustness across diverse imaging scenarios,from custom-built platforms to commercial systems.These results establish PCA-3DSIM as a flexible and practical tool for super-resolved volumetric imaging of subcellular structures,with broad potential applications in biomedical research.
基金supported by Higher Education Commission, Pakistan (Grants No. 20-1493/R&D/09)
文摘Linking similar proteins structurally is a challenging task that may help in finding the novel members of a protein family. In this respect, identification of conserved sequence can facilitate understanding and classifying the exact role of proteins. However, the exact role of these conserved elements cannot be elucidated without structural and physiochemical information. In this work, we present a novel desktop application MotViz designed for searching and analyzing the conserved sequence segments within protein structure. With MotViz, the user can extract a complete list of sequence motifs from loaded 3D structures, annotate the motifs structurally and analyze their physiochemical properties. The conservation value calculated for an individual motif can be visualized graphically. To check the efficiency, predicted motifs from the data sets of 9 protein families were analyzed and Mot^z algorithm was more efficient in comparison to other online motif prediction tools. Furthermore, a database was also integrated for storing, retrieving and performing the detailed functional annotation studies. In summary, MotViz effectively predicts motifs with high sensitivity and simultaneously visualizes them into 3D strucures. Moreover, Mot- V/z is user-friendly with optimized graphical parameters and better processing speed due to the inclusion of a database at the back end. MotViz is available at http：//www.fi-pk.corn/motviz.html.
基金funding support from:National Institutes of Health grant R01 EY032163(S.C.,L.T)National Institutes of Health grant U54 NS115266(A.C.M.)+8 种基金National Institutes of Health grant P30 AG072978(A.C.M.)National Institutes of Health grant U19 AG068753(A.C.M.)National Institutes of Health grant R01 NS125307(D.L.R.)National Institutes of Health grant RO1 AG075727(D.L.R.and IJ.B)National Institutes of Health grant RF1 AG062831(D.L.R.)Boston University Kilachand Fund Award(I.J.B.,D.L.R.,D.A.B.,and L.T.)National Institutes of Health grant R00 EB023993(HW)National Institutes of Health grant R01 NS128843(H.W.)National Institutes of Health grant U01 MH117023(S.C.,A.N.,D.A.B.)。
文摘A major challenge in neuroscience is visualizing the structure of the human brain at different scales.Traditional histology reveals micro-and meso-scale brain features but suffers from staining variability,tissue damage,and distortion,which impedes accurate 3D reconstructions.The emerging label-free serial sectioning optical coherence tomography(S-OCT)technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features.Here,we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining(DS)model.This enhanced imaging modality integrates high-throughput 3D imaging,low sample variability and high interpretability,making it suitable for 3D histology studies.We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver staining.We demonstrate DS on various human cerebral cortex samples,achieving consistent staining quality and enhancing contrast across cortical layer boundaries.Additionally,we show that Ds preserves geometry in 3D on cubic-centimeter tissue blocks,allowing for visualization of meso-scale vessel networks in the white matter.We believe that our technique has the potential for high-throughput,multiscale imaging of brain tissues and may facilitate studies of brain structures.
基金supported by NIH grants U54CA268084,U54CA261694,and R01CA228272National Science Foundation grants EFMA-1830961 and CBET-2430743+1 种基金philanthropic support from Rob and Kristin Goldman,Mr.David Sachsthe Christina Carinato Charitable Foundation.
文摘Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets like chromatin.Chromatin exhibits a hierarchical organization with structural compartments and domains at different length scales,from nanometers to micrometers.Single molecule localization microscopy(SMLM)methods,such as STORM,are essential for studying chromatin at the supra-nucleosome level due to their ability to target epigenetic marks that determine chromatin organization.Multi-label imaging of chromatin is necessary to unpack its structural complexity.However,these efforts are challenged by the high-density nuclear environment,which can affect antibody binding affinities,diffusivity and non-specific interactions.Optimizing buffer conditions,fluorophore stability,and antibody specificity is crucial for achieving effective antibody conjugates.Here,we demonstrate a sequential immunolabeling protocol that reliably enables three-color studies within the dense nuclear environment.This protocol couples multiplexed localization datasets with a robust analysis algorithm,which utilizes localizations from one target as seed points for distance,density and multi-label joint affinity measurements to explore complex organization of all three targets.Applying this multiplexed algorithm to analyze distance and joint density reveals that heterochromatin and euchromatin are not-distinct territories,but that localization of transcription and euchromatin couple with the periphery of heterochromatic clusters.This work is a crucial step in molecular imaging of the dense nuclear environment as multi-label capacity enables for investigation of complex multi-component systems like chromatin with enhanced accuracy.
文摘Biological imaging plays a pivotal role in visualizing and understanding biological structures and processes from molecular to macroscopic levels,enabling clinicians and researchers to noninvasively observe internal anatomy,detect disease at early stages,plan treatments,and monitor therapeutic outcomes.This broad field encompasses a diverse array of imaging modalities such as fluorescence,magnetic resonance imaging(MRI),and positron emission tomography(PET),each relying on distinct physical principles to extract specific biological information.Among them,Raman imaging has emerged as a molecularly specific and powerful technique capable of providing detailed chemical information and generating high-resolution two-or three-dimensional maps that visualize the multiscale distribution of specific molecular components within a sample.
