scholarly journals Microarray Core Detection by Geometric Restoration

2012 ◽  
Vol 35 (5-6) ◽  
pp. 381-393 ◽  
Author(s):  
Jimmy C. Azar ◽  
Christer Busch ◽  
Ingrid B. Carlbom
Keyword(s):  
High Throughput ◽  
Tissue Microarrays ◽  
Automated Image Analysis ◽  
High Throughput Analysis ◽  
Tissue Samples ◽  
The Core ◽  
Automated Method ◽  
Precise Shape ◽  
Diagnosis Of Cancer ◽  
Detection And Localization

Whole-slide imaging of tissue microarrays (TMAs) holds the promise of automated image analysis of a large number of histopathological samples from a single slide. This demands high-throughput image processing to enable analysis of these tissue samples for diagnosis of cancer and other conditions. In this paper, we present a completely automated method for the accurate detection and localization of tissue cores that is based on geometric restoration of the core shapes without placing any assumptions on grid geometry. The method relies on hierarchical clustering in conjunction with the Davies-Bouldin index for cluster validation in order to estimate the number of cores in the image wherefrom we estimate the core radius and refine this estimate using morphological granulometry. The final stage of the algorithm reconstructs circular discs from core sections such that these discs cover the entire region of each core regardless of the precise shape of the core. The results show that the proposed method is able to reconstruct core locations without any evidence of localization. Furthermore, the algorithm is more efficient than existing methods based on the Hough transform for circle detection. The algorithm’s simplicity, accuracy, and computational efficiency allow for automated high-throughput analysis of microarray images.

scholarly journals Software Tools for High-Throughput Analysis and Archiving of Immunohistochemistry Staining Data Obtained with Tissue Microarrays

2002 ◽  
Vol 161 (5) ◽  
pp. 1557-1565 ◽  
Author(s):  
Chih Long Liu ◽  
Wijan Prapong ◽  
Yasodha Natkunam ◽  
Ash Alizadeh ◽  
Kelli Montgomery ◽  
...  
Keyword(s):  
High Throughput ◽  
Tissue Microarrays ◽  
Software Tools ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Immunohistochemistry Staining

Characterization of Tumor-immune Microenvironment by High-throughput Image Analysis of CD8 Immunohistochemistry Combined With Modified Masson’s Trichrome

2021 ◽  
pp. 002215542110349
Author(s):  
Charles Havnar ◽  
Shari Lau ◽  
Jeffrey Hung ◽  
Jeff Eastham-Anderson ◽  
Carmina Espiritu ◽  
...  
Keyword(s):  
T Cells ◽  
High Throughput ◽  
Checkpoint Inhibitors ◽  
Cytotoxic T Cells ◽  
Mouse Tissue ◽  
Flexible Tool ◽  
Tissue Samples ◽  
Automated Method ◽  
Tumor Immune Microenvironment ◽  
Tumor Microenviroment

With the advent of checkpoint inhibitors, there is increasing need to study the dynamics of CD8+ T-cells in the tumor microenviroment. In this article, we describe a semi-automated method to quantify and interrogate spatial relationships between T-cells and collagenous stroma in human and mouse tissue samples. The assay combines CD8 immunohistochemistry with modified Masson’s trichrome. Slides are scanned and digital images are analyzed using an adjustable MATLAB algorithm, allowing for high-throughput quantification of cytotoxic T-cells and collagen. This method provides a flexible tool for unbiased quantification of T-cells and their interactions with tumor cells and tumor microenvironment in tissue samples.

scholarly journals An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5727 ◽  
Author(s):  
Jeffrey C. Berry ◽  
Noah Fahlgren ◽  
Alexandria A. Pokorny ◽  
Rebecca S. Bart ◽  
Kira M. Veley
Keyword(s):  
High Throughput ◽  
Linear Models ◽  
Correction Method ◽  
Automated Image Analysis ◽  
Plant Phenotyping ◽  
Image Brightness ◽  
Automated Method ◽  
Color Profile ◽  
High Throughput Phenotyping ◽  
High Throughput Imaging

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.

Biomarker barcodes: multiplexed microfluidic immunohistochemistry enables high-throughput analysis of tissue microarray

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Chang Hyun Cho ◽  
Minkyung Cho ◽  
Je-Kyun Park
Keyword(s):  
Tissue Microarray ◽  
High Throughput ◽  
Tissue Microarrays ◽  
High Throughput Analysis ◽  
Throughput Analysis

We present a multiplexed microfluidic immunohistochemistry (IHC) technology that enables high-throughput analysis of tissue microarrays (TMAs) using the patterns of biomarker barcodes, which consist of a series of expressed linear...

scholarly journals An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

2018 ◽  
Author(s):  
Jeffrey C. Berry ◽  
Noah Fahlgren ◽  
Alexandria A. Pokorny ◽  
Rebecca Bart ◽  
Kira M. Veley
Keyword(s):  
High Throughput ◽  
Linear Models ◽  
Correction Method ◽  
Automated Image Analysis ◽  
Plant Phenotyping ◽  
Image Brightness ◽  
Automated Method ◽  
Color Profile ◽  
High Throughput Phenotyping ◽  
High Throughput Imaging

ABSTRACTHigh-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.

scholarly journals Ultra-Fast Processing of Gigapixel Tissue MicroArray Images Using High Performance Computing

2010 ◽  
Vol 33 (5-6) ◽  
pp. 271-285 ◽  
Author(s):  
Yinhai Wang ◽  
David McCleary ◽  
Ching-Wei Wang ◽  
Paul Kelly ◽  
Jackie James ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Throughput ◽  
High Performance ◽  
Complex Analysis ◽  
Biomarker Discovery ◽  
Virtual Slide ◽  
Tissue Microarrays ◽  
High Throughput Analysis ◽  
Virtual Slides ◽  
Performance Computing

Background: Tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.Methods: A High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.Results: The automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.Conclusion: The methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research.

Elucidating the Weakly Reversible Cs-Pb-Br Perovskite Nanocrystal Reaction Network with High-Throughput Maps and Transformations

2020 ◽  
Author(s):  
Jakob Dahl ◽  
Xingzhi Wang ◽  
Xiao Huang ◽  
Emory Chan ◽  
Paul Alivisatos
Keyword(s):  
High Throughput ◽  
Dynamic Equilibrium ◽  
Reaction Network ◽  
Substantial Impact ◽  
Equilibrium Behavior ◽  
Automated Method ◽  
Lead Bromide ◽  
Different Shapes ◽  
Complex Chemistry ◽  
Lead Halide

<p>Advances in automation and data analytics can aid exploration of the complex chemistry of nanoparticles. Lead halide perovskite colloidal nanocrystals provide an interesting proving ground: there are reports of many different phases and transformations, which has made it hard to form a coherent conceptual framework for their controlled formation through traditional methods. In this work, we systematically explore the portion of Cs-Pb-Br synthesis space in which many optically distinguishable species are formed using high-throughput robotic synthesis to understand their formation reactions. We deploy an automated method that allows us to determine the relative amount of absorbance that can be attributed to each species in order to create maps of the synthetic space. These in turn facilitate improved understanding of the interplay between kinetic and thermodynamic factors that underlie which combination of species are likely to be prevalent under a given set of conditions. Based on these maps, we test potential transformation routes between perovskite nanocrystals of different shapes and phases. We find that shape is determined kinetically, but many reactions between different phases show equilibrium behavior. We demonstrate a dynamic equilibrium between complexes, monolayers and nanocrystals of lead bromide, with substantial impact on the reaction outcomes. This allows us to construct a chemical reaction network that qualitatively explains our results as well as previous reports and can serve as a guide for those seeking to prepare a particular composition and shape. </p>

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