In JoVE (2)

Other Publications (14)

Articles by David Mayerich in JoVE

 JoVE Bioengineering

Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy

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

JoVE 3248

Other articles by David Mayerich on PubMed

Visualization of Fibrous and Thread-like Data

IEEE Transactions on Visualization and Computer Graphics. Sep-Oct, 2006  |  Pubmed ID: 17080848

Thread-like structures are becoming more common in modern volumetric data sets as our ability to image vascular and neural tissue at higher resolutions improves. The thread-like structures of neurons and micro-vessels pose a unique problem in visualization since they tend to be densely packed in small volumes of tissue. This makes it difficult for an observer to interpret useful patterns from the data or trace individual fibers. In this paper we describe several methods for dealing with large amounts of thread-like data, such as data sets collected using Knife-Edge Scanning Microscopy (KESM) and Serial Block-Face Scanning Electron Microscopy (SBF-SEM). These methods allow us to collect volumetric data from embedded samples of whole-brain tissue. The neuronal and microvascular data that we acquire consists of thin, branching structures extending over very large regions. Traditional visualization schemes are not sufficient to make sense of the large, dense, complex structures encountered. In this paper, we address three methods to allow a user to explore a fiber network effectively. We describe interactive techniques for rendering large sets of neurons using self-orienting surfaces implemented on the GPU. We also present techniques for rendering fiber networks in a way that provides useful information about flow and orientation. Third, a global illumination framework is used to create high-quality visualizations that emphasize the underlying fiber structure. Implementation details, performance, and advantages and disadvantages of each approach are discussed.

Visualization of Cellular and Microvascular Relationships

IEEE Transactions on Visualization and Computer Graphics. Nov-Dec, 2008  |  Pubmed ID: 18989017

Understanding the structure of microvasculature structures and their relationship to cells in biological tissue is an important and complex problem. Brain microvasculature in particular is known to play an important role in chronic diseases. However, these networks are only visible at the microscopic level and can span large volumes of tissue. Due to recent advances in microscopy, large volumes of data can be imaged at the resolution necessary to reconstruct these structures. Due to the dense and complex nature of microscopy data sets, it is important to limit the amount of information displayed. In this paper, we describe methods for encoding the unique structure of microvascular data, allowing researchers to selectively explore microvascular anatomy. We also identify the queries most useful to researchers studying microvascular and cellular relationships. By associating cellular structures with our microvascular framework, we allow researchers to explore interesting anatomical relationships in dense and complex data sets.

Hardware Accelerated Segmentation of Complex Volumetric Filament Networks

IEEE Transactions on Visualization and Computer Graphics. Jul-Aug, 2009  |  Pubmed ID: 19423890

We present a framework for segmenting and storing filament networks from scalar volume data. Filament networks are encountered more and more commonly in biomedical imaging due to advances in high-throughput microscopy. These data sets are characterized by a complex volumetric network of thin filaments embedded in a scalar volume field. High-throughput microscopy volumes are also difficult to manage since they can require several terabytes of storage, even though the total volume of the embedded structure is much smaller. Filaments in microscopy data sets are difficult to segment because their diameter is often near the sampling resolution of the microscope, yet these networks can span large regions of the data set. We describe a novel method to trace filaments through scalar volume data sets that is robust to both noisy and undersampled data. We use graphics hardware to accelerate the tracing algorithm, making it more useful for large data sets. After the initial network is traced, we use an efficient encoding scheme to store volumetric data pertaining to the network.

Fast Macro-scale Transmission Imaging of Microvascular Networks Using KESM

Biomedical Optics Express. Oct, 2011  |  Pubmed ID: 22091443

Accurate microvascular morphometric information has significant implications in several fields, including the quantification of angiogenesis in cancer research, understanding the immune response for neural prosthetics, and predicting the nature of blood flow as it relates to stroke. We report imaging of the whole mouse brain microvascular system at resolutions sufficient to perform accurate morphometry. Imaging was performed using Knife-Edge Scanning Microscopy (KESM) and is the first example of this technique that can be directly applied to clinical research. We are able to achieve ≈ 0.7μm resolution laterally with 1μm depth resolution using serial sectioning. No alignment was necessary and contrast was sufficient to allow segmentation and measurement of vessels.

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.

NetMets: Software for Quantifying and Visualizing Errors in Biological Network Segmentation

BMC Bioinformatics. 2012  |  Pubmed ID: 22607549

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization.

Recovery of Absorption Spectra from Fourier Transform Infrared (FT-IR) Microspectroscopic Measurements of Intact Spheres

Applied Spectroscopy. May, 2013  |  Pubmed ID: 23643044

An infrared spectrum recorded from a microscopic sample depends on spectral properties of the constituent material as well as on morphology. Many samples or domains within heterogeneous materials can be idealized as spheres, in which both scattering and absorption from the three-dimensional shape affect the recorded spectrum. Spectra recorded from such objects may be altered to such an extent that they bear little resemblance to spectra recorded from the bulk material; there are no methods, however, to reconcile the two from first principles. Here we provide the mathematical description of the optical physics underlying light-spherical sample interaction within an instrument. We use the developed analytical expressions to predict recorded data from spheres using Fourier transform infrared (FT-IR) spectroscopic imaging. Recorded spectra are shown to depend strongly on the size of the sphere as well as the optical arrangement of the instrument. Next, we present theory and experiments demonstrating the recovery of the complex refractive index of the material using data recorded from a sphere. The effects of the sample morphology on the measured spectra can be removed, and using the imaginary part of the index, the shape-independent IR absorption spectrum of the material is recovered.

Real-time Interactive Data Mining for Chemical Imaging Information: Application to Automated Histopathology

BMC Bioinformatics. May, 2013  |  Pubmed ID: 23651487

Vibrational spectroscopic imaging is now used in several fields to acquire molecular information from microscopically heterogeneous systems. Recent advances have led to promising applications in tissue analysis for cancer research, where chemical information can be used to identify cell types and disease. However, recorded spectra are affected by the morphology of the tissue sample, making identification of chemical structures difficult.

Rapid Spectral-domain Localization

Optics Express. May, 2013  |  Pubmed ID: 23736501

We present a method to dynamically image structures at nanometer spatial resolution with far-field instruments. We propose the use of engineered nanoprobes with distinguishable spectral responses and the measurement of coherent scattering, rather than fluorescence. Approaches such as PALM/STORM have relied on the rarity of emission events in time to distinguish signals from distinct probes. By distinguishing signals in the spectral domain, we enable the acquisition of data in a multiplex fashion and thus circumvent the fundamental problem of slow data acquisition of current techniques. The described method has the potential to image dynamic systems with a spatial resolution only limited to the size of the scattering probes.

On the Importance of Image Formation Optics in the Design of Infrared Spectroscopic Imaging Systems

The Analyst. Aug, 2014  |  Pubmed ID: 24936526

Infrared spectroscopic imaging provides micron-scale spatial resolution with molecular contrast. While recent work demonstrates that sample morphology affects the recorded spectrum, considerably less attention has been focused on the effects of the optics, including the condenser and objective. This analysis is extremely important, since it will be possible to understand effects on recorded data and provides insight for reducing optical effects through rigorous microscope design. Here, we present a theoretical description and experimental results that demonstrate the effects of commonly-employed cassegranian optics on recorded spectra. We first combine an explicit model of image formation and a method for quantifying and visualizing the deviations in recorded spectra as a function of microscope optics. We then verify these simulations with measurements obtained from spatially heterogeneous samples. The deviation of the computed spectrum from the ideal case is quantified via a map which we call a deviation map. The deviation map is obtained as a function of optical elements by systematic simulations. Examination of deviation maps demonstrates that the optimal optical configuration for minimal deviation is contrary to prevailing practice in which throughput is maximized for an instrument without a sample. This report should be helpful for understanding recorded spectra as a function of the optics, the analytical limits of recorded data determined by the optical design, and potential routes for optimization of imaging systems.

Stain-less Staining for Computed Histopathology

Technology. Mar, 2015  |  Pubmed ID: 26029735

Dyes such as hematoxylin and eosin (H&E) and immunohistochemical stains have been increasingly used to visualize tissue composition in research and clinical practice. We present an alternative approach to obtain the same information using stain-free chemical imaging. Relying on Fourier transform infrared (FT-IR) spectroscopic imaging and computation, stainless computed histopathology can enable a rapid, digital, quantitative and non-perturbing visualization of morphology and multiple molecular epitopes simultaneously in a variety of research and clinical pathology applications.

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.

Compositional Prior Information in Computed Infrared Spectroscopic Imaging

Journal of the Optical Society of America. A, Optics, Image Science, and Vision. Jun, 2015  |  Pubmed ID: 26367047

Compositional prior information is used to bridge a gap in the theory between optical coherence tomography (OCT), which provides high-resolution structural images by neglecting spectral variation, and imaging spectroscopy, which provides only spectral information without significant regard to structure. A constraint is proposed in which it is assumed that a sample is composed of N distinct materials with known spectra, allowing the structural and spectral composition of the sample to be determined with a number of measurements on the order of N. We present a forward model for a sample with heterogeneities along the optical axis and show through simulation that the N-species constraint allows unambiguous inversion of Fourier transform interferometric data within the spatial frequency passband of the optical system. We then explore the stability and limitations of this model and extend it to a general 3D heterogeneous sample.

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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