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Articles by David Mayerich in JoVE
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Изготовление образцов, Imaging, и анализ протоколов для ножа сканирующей микроскопии
Yoonsuck Choe1, David Mayerich2, Jaerock Kwon3, Daniel E. Miller1, Chul Sung1, Ji Ryang Chung1, Todd Huffman4, John Keyser1, Louise C. Abbott5
1Department of Computer Science and Engineering, Texas A&M University, 2Beckman Institute for Advanced Science and Technology, University of Illinois, 3Department of Electrical and Computer Engineering, Kettering University, 43Scan, 5Department of Veterinary Integrative Biosciences, Texas A&M University
Полный процесс от мозга подготовки образца до серийного секционирования визуализации с использованием ножа сканирующий микроскоп, для визуализации и анализа данных описывается. Эта техника в настоящее время используется для получения данных мозга мыши, но она применима и к другим органам, другим видам.
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Other articles by David Mayerich on PubMed
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Multiscale Exploration of Mouse Brain Microstructures Using the Knife-edge Scanning Microscope Brain Atlas
Frontiers in Neuroinformatics.
2011 |
Pubmed ID: 22275895 Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions.
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High Definition Infrared Spectroscopic Imaging for Lymph Node Histopathology
PloS One.
2015 |
Pubmed ID: 26039216 Chemical imaging is a rapidly emerging field in which molecular information within samples can be used to predict biological function and recognize disease without the use of stains or manual identification. In Fourier transform infrared (FT-IR) spectroscopic imaging, molecular absorption contrast provides a large signal relative to noise. Due to the long mid-IR wavelengths and sub-optimal instrument design, however, pixel sizes have historically been much larger than cells. This limits both the accuracy of the technique in identifying small regions, as well as the ability to visualize single cells. Here we obtain data with micron-sized sampling using a tabletop FT-IR instrument, and demonstrate that the high-definition (HD) data lead to accurate identification of multiple cells in lymph nodes that was not previously possible. Highly accurate recognition of eight distinct classes - naïve and memory B cells, T cells, erythrocytes, connective tissue, fibrovascular network, smooth muscle, and light and dark zone activated B cells was achieved in healthy, reactive, and malignant lymph node biopsies using a random forest classifier. The results demonstrate that cells currently identifiable only through immunohistochemical stains and cumbersome manual recognition of optical microscopy images can now be distinguished to a similar level through a single IR spectroscopic image from a lymph node biopsy.
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SIproc: an Open-source Biomedical Data Processing Platform for Large Hyperspectral Images
The Analyst.
Dec, 2016 |
Pubmed ID: 27924319 There has recently been significant interest within the vibrational spectroscopy community to apply quantitative spectroscopic imaging techniques to histology and clinical diagnosis. However, many of the proposed methods require collecting spectroscopic images that have a similar region size and resolution to the corresponding histological images. Since spectroscopic images contain significantly more spectral samples than traditional histology, the resulting data sets can approach hundreds of gigabytes to terabytes in size. This makes them difficult to store and process, and the tools available to researchers for handling large spectroscopic data sets are limited. Fundamental mathematical tools, such as MATLAB, Octave, and SciPy, are extremely powerful but require that the data be stored in fast memory. This memory limitation becomes impractical for even modestly sized histological images, which can be hundreds of gigabytes in size. In this paper, we propose an open-source toolkit designed to perform out-of-core processing of hyperspectral images. By taking advantage of graphical processing unit (GPU) computing combined with adaptive data streaming, our software alleviates common workstation memory limitations while achieving better performance than existing applications.
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