Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transformi...Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.展开更多
Pluta polarizing interference microscope was used to follow the crazing that occur on the surface of stretched polypropylene fibres at different drawing conditions. The samples were stretched until crazing initiated, ...Pluta polarizing interference microscope was used to follow the crazing that occur on the surface of stretched polypropylene fibres at different drawing conditions. The samples were stretched until crazing initiated, and then craze propagation was monitored as a function of drawing speed and test temperature. The effect of craze dimension on their propagation velocity was taken into account. Three-dimensional birefringence profile for crazed polypropylene fibre has been demonstrated to investigate the birefringence of crazed fibre at different test times for fixed drawing speed value. Also the mean birefringence values of crazed polypropylene fibres were calculated and the results showed that, these values increased with the areal craze density. Video images were used to calculate the craze velocity. Optical micrographs and microinterferograms were presented for demonstrations.展开更多
Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-...Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.展开更多
The aim of this study was to compare the sperm nuclear and acrosomal morphometry of three species of domestic artiodactyls; cattle （Bos taurus）, sheep （Ovis aries）, and pigs （Sus scrofa）. Semen smears of twenty ...The aim of this study was to compare the sperm nuclear and acrosomal morphometry of three species of domestic artiodactyls; cattle （Bos taurus）, sheep （Ovis aries）, and pigs （Sus scrofa）. Semen smears of twenty ejaculates from each species were fixed and labeled with a propidium iodide-Pisum sativum agglutinin （PI/PSA） combination. Digital images of the sperm nucleus, acrosome, and whole sperm head were captured and analyzed. The use of the PI/PSA combination and CASA-Morph fluorescence-based method allowed the capture, morphometric analysis, and differentiation of most sperm nuclei, acrosomes and whole heads, and the assessment of acrosomal integrity with a high precision in the three species studied. For the size of the head and nuclear area, the relationship between the three species may be summarized as bull 〉 ram 〉 boar. However, for the other morphometric parameters （length, width, and perimeter）, there were differences in the relationships between species for sperm nuclei and whole sperm heads. Bull sperm acrosomes were clearly smaller than those in the other species studied and covered a smaller proportion of the sperm head. The acrosomal morphology, small in the bull, large and broad in the sheep, and large, long, and with a pronounced equatorial segment curve in the boar, was species-characteristic. It was concluded that there are clear variations in the size and shape of the sperm head components between the three species studied, the acrosome being the structure showing the most variability, allowing a clear distinction of the spermatozoa of each species.展开更多
Hadamard transform spatial multiplexed imaging technique is combined with fluorescence microscope and an instrument of Hadamard transform microscope fluorescence image analysis is developed. Images acquired by this in...Hadamard transform spatial multiplexed imaging technique is combined with fluorescence microscope and an instrument of Hadamard transform microscope fluorescence image analysis is developed. Images acquired by this instrument can provide a lot of useful information simultaneously, including three-dimensional Hadamard transform microscope cell fluorescence image, the fluorescence intensity and fluorescence distribution of a cell, the background signal intensity and the signal/noise ratio, etc.展开更多
In this paper,we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope(SEM).This is done by c...In this paper,we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope(SEM).This is done by coupling supervised and unsupervised learning approaches.We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy.Then,we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales(from 1μm to 2μm).Finally,we compare different clustering methods to uncover intrinsic structures in the images.展开更多
扫描电子显微镜(scanning electron microscope,SEM)在材料表征领域具有广泛的应用前景,然而所获得的图像通常难以直接提取定量信息。针对一种共晶高熵合金的扫描电镜图像,提出了一种基于机器学习和图像分割技术的自动化、定量化分析方...扫描电子显微镜(scanning electron microscope,SEM)在材料表征领域具有广泛的应用前景,然而所获得的图像通常难以直接提取定量信息。针对一种共晶高熵合金的扫描电镜图像,提出了一种基于机器学习和图像分割技术的自动化、定量化分析方法,该方法能够有效测量共晶高熵合金板条状区域的面积、长度、宽度、周长以及不同组分的占比。实验结果表明,本研究所提出的方法在高熵合金图像上具有良好的鲁棒性和准确性,为研究高熵合金材料的表面结构提供了重要的技术支持。展开更多
基金funding support from the US National Science Foundation(2229092)supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship,a program of Schmidt Sciences,LLC.
文摘Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.
文摘Pluta polarizing interference microscope was used to follow the crazing that occur on the surface of stretched polypropylene fibres at different drawing conditions. The samples were stretched until crazing initiated, and then craze propagation was monitored as a function of drawing speed and test temperature. The effect of craze dimension on their propagation velocity was taken into account. Three-dimensional birefringence profile for crazed polypropylene fibre has been demonstrated to investigate the birefringence of crazed fibre at different test times for fixed drawing speed value. Also the mean birefringence values of crazed polypropylene fibres were calculated and the results showed that, these values increased with the areal craze density. Video images were used to calculate the craze velocity. Optical micrographs and microinterferograms were presented for demonstrations.
文摘Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.
文摘The aim of this study was to compare the sperm nuclear and acrosomal morphometry of three species of domestic artiodactyls; cattle （Bos taurus）, sheep （Ovis aries）, and pigs （Sus scrofa）. Semen smears of twenty ejaculates from each species were fixed and labeled with a propidium iodide-Pisum sativum agglutinin （PI/PSA） combination. Digital images of the sperm nucleus, acrosome, and whole sperm head were captured and analyzed. The use of the PI/PSA combination and CASA-Morph fluorescence-based method allowed the capture, morphometric analysis, and differentiation of most sperm nuclei, acrosomes and whole heads, and the assessment of acrosomal integrity with a high precision in the three species studied. For the size of the head and nuclear area, the relationship between the three species may be summarized as bull 〉 ram 〉 boar. However, for the other morphometric parameters （length, width, and perimeter）, there were differences in the relationships between species for sperm nuclei and whole sperm heads. Bull sperm acrosomes were clearly smaller than those in the other species studied and covered a smaller proportion of the sperm head. The acrosomal morphology, small in the bull, large and broad in the sheep, and large, long, and with a pronounced equatorial segment curve in the boar, was species-characteristic. It was concluded that there are clear variations in the size and shape of the sperm head components between the three species studied, the acrosome being the structure showing the most variability, allowing a clear distinction of the spermatozoa of each species.
基金Project supported by the National Natural Science Foundation of China.
文摘Hadamard transform spatial multiplexed imaging technique is combined with fluorescence microscope and an instrument of Hadamard transform microscope fluorescence image analysis is developed. Images acquired by this instrument can provide a lot of useful information simultaneously, including three-dimensional Hadamard transform microscope cell fluorescence image, the fluorescence intensity and fluorescence distribution of a cell, the background signal intensity and the signal/noise ratio, etc.
基金This work has been done within the NFFA-EUROPE project and has received funding from the European Union’s Horizon 2020 Research and Innovation Program under grant agreement No.654360 NFFA-EUROPE.
文摘In this paper,we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope(SEM).This is done by coupling supervised and unsupervised learning approaches.We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy.Then,we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales(from 1μm to 2μm).Finally,we compare different clustering methods to uncover intrinsic structures in the images.
文摘扫描电子显微镜(scanning electron microscope,SEM)在材料表征领域具有广泛的应用前景,然而所获得的图像通常难以直接提取定量信息。针对一种共晶高熵合金的扫描电镜图像,提出了一种基于机器学习和图像分割技术的自动化、定量化分析方法,该方法能够有效测量共晶高熵合金板条状区域的面积、长度、宽度、周长以及不同组分的占比。实验结果表明,本研究所提出的方法在高熵合金图像上具有良好的鲁棒性和准确性,为研究高熵合金材料的表面结构提供了重要的技术支持。
