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A new self-made digital slide scanner and microscope for imaging and quantification of fluorescent microspheres
摘要： Objective: A low-cost microscope slide scanner was constructed for the purpose of digital imaging of newborn piglet brain tissue and to quantify fluorescent microspheres in tissue. Methods: Using a standard digital single-lens reflex (DSLR) camera, fluorescent imaging of newborn piglet brain tissue was performed. A computer algorithm available for download was created to detect fluorescent microspheres in the brain tissue slides and to calculate regional cerebral blood flow (rCBF). The precision of the algorithm was tested by comparing with manual counting of the fluorescent microspheres. Finally, bright-field imaging was tested by adding light diffuser film. Results: Cost of the slide scanner was a fraction of the cost of a commercial slide scanner. The slide scanner was able to image a large number of tissue slides in a semiautomatic manner and provided a large field of view (FOV) of 101 mm2 combined with a resolution of 2.9 μm. The mean difference (SD) between manual and automatic counts was in absolute numbers 0.32 (1.5) microspheres ranging from -5 to 5 microspheres per slide. The relative total difference between automatic and manual counts was -3.1%. Conclusions: A slide scanner was constructed and an automatic algorithm to detect fluorescent microspheres in tissue was developed and validated and showed an acceptable difference to “gold standard” manual counting. The slide scanner can be regarded as a low-cost alternative for researchers when digital slide imaging and quantification of fluorescent microspheres are needed.
关键词： Slide scanner,Fluorescence,Bright-field,Microspheres,Microscopy,Cerebral blood flow
更新于2025-09-23 15:23:52
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Large-scale Multi-class Image-based Cell Classification with Deep Learning
摘要： Recent advances in ultra-high-throughput optical microscopy have enabled a new generation of cell classification methodologies using image-based cell phenotypes alone. In contrast to the current single-cell analysis techniques that rely solely on slow and costly genetic/epigenetic analyses, these image-based classification methods allow morphological profiling and screening of thousands or even millions of single cells at a fraction of the cost. Furthermore, they have demonstrated the statistical significance required for understanding the role of cell heterogeneity in diverse biological applications, ranging from cancer screening to drug candidate identification/validation processes. This work examines the efficacies and opportunities presented by machine learning algorithms in processing large-scale datasets with millions of label-free cell images. An automatic single-cell classification framework using a convolutional neural network (CNN) has been developed. A comparative analysis of its efficiency in classifying large datasets against conventional k-nearest neighbors (kNN) and support vector machine (SVM) based methods is also presented. Experiments have shown that (i) our proposed framework can identify multiple types of cells with over 99 % accuracy based on label-free bright-field images efficiently; (ii) CNN-based models perform well and relatively stable against changes in data volume compared with kNN and SVM.
关键词： Convolutional neural network,Bright field imaging,Multiclass classification,Cell classification
更新于2025-09-23 15:21:01
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European Microscopy Congress 2016: Proceedings || Analytical electron microscopy characterization of light-emitting diodes based on ordered InGaN nanocolumns
摘要： Self-assembled nanocolumns (NCs) with InGaN/GaN disks constitute an alternative to conventional light emitting diodes (LED) planar devices [1]. However, their efficiency and reliability are hindered by a strong dispersion of electrical characteristics among individual nanoLED. Polychromatic emission derives from an inhomogeneous distribution of indium concentration due to the inherent tendency of InGaN alloys to develop composition fluctuations as a function of the polarity of the growth crystallographic planes [2]. The recent development of selective area growth of NCs by molecular beam epitaxy has allowed the achieving of highly homogeneous and controllable GaN/InGaN NCs with improved crystalline quality and higher control over the indium distribution [3]. In this work, we present the characterization performed on LEDs based on ordered NCs with InGaN active disks (figure 1). The detailed structural characterization of the nanostructures has been performed by scanning transmission electron microscopy (STEM) carried out on an aberration-corrected JEOL-JEMARM200 microscope. High crystal quality of the NCs is set by the analysis of atomically-resolved high angle annular dark field (HAADF) images. The indium distribution within the InGaN disks is studied by EDS elemental mapping while the polarity of the semiconductor NCs is followed by locating the nitrogen atomic columns in annular bright field (ABF) images while (figure2). Direct correlation of the optical and structural properties on a nanometer-scale was achieved using low temperature cathodoluminescence (CL) spectroscopy in an FEI STEM Tecnai F20 [4].
关键词： nanowires,EDS,annular bright field,InGaN,LEDs,atomic-resolution STEM
更新于2025-09-12 10:27:22
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Detection of oxygen sub-lattice ordering in A-site deficient perovskites through monochromated core-loss EELS mapping
摘要： Perovskite oxides are widely studied for a variety of applications, from thermoelectrics to fuel cells. Part of the attraction lies in the fact that perovskite ceramics are relatively easy to dope chemically over a wide range of compositions, resulting in various degrees of structural ordering. As a consequence, the properties and functionalities of such materials can be readily tailored. For instance in systems proposed for thermoelectric applications, the presence of superlattices, or domain boundaries vacancies can suppress the thermal conductivity due to increased phonon scattering. Understanding therefore the mechanisms behind the formation of such types of ordering in ceramic systems is crucial for their implementation in engineering applications. Here, we report on an A-site deficient perovskite system based on the Nd2/3xTiO3 double perovskite. This system, a candidate for thermoelectric applications, has attracted significant attention due to the presence of a peculiar superstructure originating in part in A-site cation vacancy ordering. Using aberration corrected Scanning Transmission Electron Microscopy we investigate a series of Nd2/3xTiO3 ceramics engineered to possess different degrees of A-site cation-vacancy ordering and as a result vastly different thermoelectric properties. Annular Bright Field Imaging of the [110] orientation, preformed in the Nion UltraSTEM 100TM reveals the presence of tilting domains in the TiO6 sub lattice, dependent on the A-site occupancy. Furthermore, advanced image analysis of the electron micrographs was used to measure local distortions in the TiO6 lattice. The presence of these octahedral distortions was further investigated by employing atomically resolved monochromated core loss Electron Energy Loss measurements, acquired with an energy resolution better than 0.100eV, using the Nion UltraSTEM 100MC TM instrument. With this approach it is not only possible to map individual components of the Ti L2,3 near edge fine structure, but also fine local changes in the ELNES; subtle changes Ti L2,3 pre-peak intensity – usually not discernible in conventional EELS measurements as well as changes in the Ti L3 eg/tg and tg L3/L2 intensity ratios all indicative of local TiO6 distortions.
关键词： Annular Bright Field Imaging,aberration corrected Scanning Transmission Electron Microscopy,Nd2/3xTiO3,structural ordering,Electron Energy Loss measurements,thermoelectrics,fuel cells,A-site deficient perovskite,phonon scattering,perovskite oxides
更新于2025-09-09 09:28:46