scholarly journals Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python

2014 ◽  
Vol 8 ◽  
Author(s):  
Nicolas Rey-Villamizar ◽  
Vinay Somasundar ◽  
Murad Megjhani ◽  
Yan Xu ◽  
Yanbin Lu ◽  
...  
Keyword(s):  
Image Analysis ◽  
Brain Tissue ◽  
Large Scale ◽  
Automated Image Analysis ◽  
Neuroprosthetic Devices

scholarly journals A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
M. Elena Garcia-Pardo ◽  
Jeremy C. Simpson ◽  
Niamh C. O’Sullivan
Keyword(s):  
Endoplasmic Reticulum ◽  
Image Analysis ◽  
Mammalian Cells ◽  
Large Scale ◽  
Morphological Changes ◽  
Methodological Approach ◽  
Automated Image Analysis ◽  
Adherent Cell ◽  
Analysis Pipeline ◽  
Cultured Human Cells

Abstract Background In mammalian cells the endoplasmic reticulum (ER) comprises a highly complex reticular morphology that is spread throughout the cytoplasm. This organelle is of particular interest to biologists, as its dysfunction is associated with numerous diseases, which often manifest themselves as changes to the structure and organisation of the reticular network. Due to its complex morphology, image analysis methods to quantitatively describe this organelle, and importantly any changes to it, are lacking. Results In this work we detail a methodological approach that utilises automated high-content screening microscopy to capture images of cells fluorescently-labelled for various ER markers, followed by their quantitative analysis. We propose that two key metrics, namely the area of dense ER and the area of polygonal regions in between the reticular elements, together provide a basis for measuring the quantities of rough and smooth ER, respectively. We demonstrate that a number of different pharmacological perturbations to the ER can be quantitatively measured and compared in our automated image analysis pipeline. Furthermore, we show that this method can be implemented in both commercial and open-access image analysis software with comparable results. Conclusions We propose that this method has the potential to be applied in the context of large-scale genetic and chemical perturbations to assess the organisation of the ER in adherent cell cultures.

scholarly journals An Automated, Single Cell Quantitative Imaging Microscopy Approach to Assess Micronucleus Formation, Genotoxicity and Chromosome Instability

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 344 ◽  
Author(s):  
Chloe C. Lepage ◽  
Laura L. Thompson ◽  
Bradley Larson ◽  
Kirk J. McManus
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Large Scale ◽  
Quantitative Imaging ◽  
Chromosome Instability ◽  
Genotoxic Stress ◽  
Automated Image Analysis ◽  
Micronucleus Formation ◽  
Distinct Cell ◽  
Efficient Alternative

Micronuclei are small, extranuclear bodies that are distinct from the primary cell nucleus. Micronucleus formation is an aberrant event that suggests a history of genotoxic stress or chromosome mis-segregation events. Accordingly, assays evaluating micronucleus formation serve as useful tools within the fields of toxicology and oncology. Here, we describe a novel micronucleus formation assay that utilizes a high-throughput imaging platform and automated image analysis software for accurate detection and rapid quantification of micronuclei at the single cell level. We show that our image analysis parameters are capable of identifying dose-dependent increases in micronucleus formation within three distinct cell lines following treatment with two established genotoxic agents, etoposide or bleomycin. We further show that this assay detects micronuclei induced through silencing of the established chromosome instability gene, SMC1A. Thus, the micronucleus formation assay described here is a versatile and efficient alternative to more laborious cytological approaches, and greatly increases throughput, which will be particularly beneficial for large-scale chemical or genetic screens.

scholarly journals Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0245638
Author(s):  
Sripad Ram ◽  
Pamela Vizcarra ◽  
Pamela Whalen ◽  
Shibing Deng ◽  
C. L. Painter ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Large Scale ◽  
Digital Image Analysis ◽  
Tumor Resection ◽  
Automated Image Analysis ◽  
Current Standard ◽  
Tissue Sections ◽  
Slide Image ◽  
Standard Of Practice

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

scholarly journals Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

2017 ◽  
Author(s):  
Jonathan A. Atkinson ◽  
Guillaume Lobet ◽  
Manuel Noll ◽  
Patrick E. Meyer ◽  
Marcus Griffiths ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Large Scale ◽  
Learning Algorithms ◽  
Root Systems ◽  
Machine Learning Algorithms ◽  
Automated Image Analysis ◽  
Genetic Studies ◽  
Image Descriptors ◽  
Architectural Traits

AbstractBackgroundGenetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming).FindingsWe trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method.ConclusionsWe have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.

Automation strategies for large-scale 3D image analysis

2016 ◽  
Vol 64 (7) ◽  
Author(s):  
Johannes Stegmaier ◽  
Benjamin Schott ◽  
Eduard Hübner ◽  
Manuel Traub ◽  
Maryam Shahid ◽  
...  
Keyword(s):  
Image Analysis ◽  
Large Scale ◽  
Imaging Techniques ◽  
Image Data ◽  
Automated Image Analysis ◽  
Future Research ◽  
Time Resolved ◽  
Processing Strategies ◽  
Spatio Temporal ◽  
Automation Strategies

AbstractNew imaging techniques enable visualizing and analyzing a multitude of unknown phenomena in many areas of science at high spatio-temporal resolution. The rapidly growing amount of image data, however, can hardly be analyzed manually and, thus, future research has to focus on automated image analysis methods that allow one to reliably extract the desired information from large-scale multidimensional image data. Starting with infrastructural challenges, we present new software tools, validation benchmarks and processing strategies that help coping with large-scale image data. The presented methods are illustrated on typical problems observed in developmental biology that can be answered, e.g., by using time-resolved 3D microscopy images.

scholarly journals Priming of pioneer tree Guazuma ulmifolia (Malvaceae) seeds evaluated by an automated computer image analysis

2010 ◽  
Vol 67 (3) ◽  
pp. 274-279 ◽  
Author(s):  
Pedro Henrique Santin Brancalion ◽  
David Tay ◽  
Ana Dionisia da Luz Coelho Novembre ◽  
Ricardo Ribeiro Rodrigues ◽  
Júlio Marcos Filho
Keyword(s):  
Image Analysis ◽  
Large Scale ◽  
Restoration Ecology ◽  
Seedling Emergence ◽  
Initial Moisture Content ◽  
Direct Seeding ◽  
Automated Image Analysis ◽  
Pioneer Tree ◽  
Guazuma Ulmifolia ◽  
Peg 8000

Direct seeding is one of the most promising methods in restoration ecology, but low field seedling emergence from pioneer tree seeds still reduces its large scale applicability. The aim of this research was to evaluate seed priming for the pioneer tree species Guazuma ulmifolia. Priming treatments were selected based on seed hydration curves in water and in PEG 8000 solution. Seeds were primed in water for 16 h and in Polyethylene glycol - PEG 8000 (-0.8 MPa for 56 and 88 h) at 20ºC to reach approximately 30% water content. Half of the seed sample of each treatment was dried back to the initial moisture content (7.2%); both dried and non-dried primed seeds as well as the unprimed seeds (control) were tested for germination (percentage and rate) and vigor (electrical conductivity of seed leachates). Seedling emergence percentage and rate were evaluated under greenhouse conditions, while seedling length and uniformity of seedling development were estimated using the automated image analysis software SVIS®. Primed seeds showed the highest physiological potential, which was mainly demonstrated by image analysis. Fresh or dried primed seeds in water for 16 h and in PEG (-0.8 MPa) for 56 h, and fresh primed seeds in PEG for 88 h, improved G. ulmifolia germination performance. It is suggested that these treatments were promising to enhance efficiency of stand establishment of this species by direct seeding in restoration ecology programs.

scholarly journals Pixelwise H-score: a novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images

2021 ◽  
Author(s):  
Sripad Ram ◽  
Pamela Vizcarra ◽  
Pamela Whalen ◽  
Shibing Deng ◽  
CL Painter ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Large Scale ◽  
Digital Image Analysis ◽  
Tumor Resection ◽  
Automated Image Analysis ◽  
Current Standard ◽  
Tissue Sections ◽  
Slide Image ◽  
Standard Of Practice

ABSTRACTImmunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

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