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 Large-Scale Image Analysis for Investigating Spatio-Temporal Changes in Nuclear DNA Damage Caused by Nitrogen Atmospheric Pressure Plasma Jets

2020 ◽  
Vol 21 (11) ◽  
pp. 4127
Author(s):  
Xu Han ◽  
James Kapaldo ◽  
Yueying Liu ◽  
M. Sharon Stack ◽  
Elahe Alizadeh ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Dna Damage ◽  
Image Analysis ◽  
Cancer Cells ◽  
Atmospheric Pressure ◽  
Large Scale ◽  
Nuclear Dna ◽  
Atmospheric Pressure Plasma ◽  
Plasma Jet ◽  
Spatio Temporal

The effective clinical application of atmospheric pressure plasma jet (APPJ) treatments requires a well-founded methodology that can describe the interactions between the plasma jet and a treated sample and the temporal and spatial changes that result from the treatment. In this study, we developed a large-scale image analysis method to identify the cell-cycle stage and quantify damage to nuclear DNA in single cells. The method was then tested and used to examine spatio-temporal distributions of nuclear DNA damage in two cell lines from the same anatomic location, namely the oral cavity, after treatment with a nitrogen APPJ. One cell line was malignant, and the other, nonmalignant. The results showed that DNA damage in cancer cells was maximized at the plasma jet treatment region, where the APPJ directly contacted the sample, and declined radially outward. As incubation continued, DNA damage in cancer cells decreased slightly over the first 4 h before rapidly decreasing by approximately 60% at 8 h post-treatment. In nonmalignant cells, no damage was observed within 1 h after treatment, but damage was detected 2 h after treatment. Notably, the damage was 5-fold less than that detected in irradiated cancer cells. Moreover, examining damage with respect to the cell cycle showed that S phase cells were more susceptible to DNA damage than either G1 or G2 phase cells. The proposed methodology for large-scale image analysis is not limited to APPJ post-treatment applications and can be utilized to evaluate biological samples affected by any type of radiation, and, more so, the cell-cycle classification can be used on any cell type with any nuclear DNA staining.

Observation of Large-Scale Structures in Unsaturated Materials

1990 ◽  
Vol 195 ◽  
Author(s):  
J. E. Maneval ◽  
M.J. Mccarthy ◽  
S. Whitaker
Keyword(s):  
Magnetic Resonance Imaging ◽  
Nuclear Magnetic Resonance ◽  
Large Scale ◽  
Imaging Techniques ◽  
Image Data ◽  
Small Scale ◽  
Resonance Imaging ◽  
Specific System ◽  
Large Scale Structures ◽  
Scale Structures

ABSTRACTWe report here the use of nuclear magnetic resonance imaging in the observation of liquid-phase fraction distributions in a partially-wetted sample of glass beads. By combiningboth large- and small-scale imaging techniques, we can study the transition from local-averaged saturations to large-scale averaged saturations. The image data allows us to assess the utility of the large-scale measurements We comment on the reliability and generality of the measurements for our specific system.

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 Time-resolved connectome of the five-factor model of personality

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
L. Passamonti ◽  
R. Riccelli ◽  
I. Indovina ◽  
A. Duggento ◽  
A. Terracciano ◽  
...  
Keyword(s):  
Large Scale ◽  
Ad Hoc ◽  
Factor Model ◽  
Five Factor Model ◽  
Future Research ◽  
Time Resolved ◽  
Personality Differences ◽  
Network Time ◽  
Large Scale Networks ◽  
The Individual

Abstract The human brain is characterized by highly dynamic patterns of functional connectivity. However, it is unknown whether this time-variant ‘connectome’ is related to the individual differences in the behavioural and cognitive traits described in the five-factor model of personality. To answer this question, inter-network time-variant connectivity was computed in n = 818 healthy people via a dynamical conditional correlation model. Next, network dynamicity was quantified throughout an ad-hoc measure (T-index) and the generalizability of the multi-variate associations between personality traits and network dynamicity was assessed using a train/test split approach. Conscientiousness, reflecting enhanced cognitive and emotional control, was the sole trait linked to stationary connectivity across several circuits such as the default mode and prefronto-parietal network. The stationarity in the ‘communication’ across large-scale networks offers a mechanistic description of the capacity of conscientious people to ‘protect’ non-immediate goals against interference over-time. This study informs future research aiming at developing more realistic models of the brain dynamics mediating personality differences.

Fine-Aggregate Angularity: Automated Image Analysis Approach

2000 ◽  
Vol 1721 (1) ◽  
pp. 66-72 ◽  
Author(s):  
Eyad Masad ◽  
Joe W. Button ◽  
Tom Papagiannakis
Keyword(s):  
Image Analysis ◽  
Visual Analysis ◽  
Imaging Techniques ◽  
Permanent Deformation ◽  
Asphalt Mixtures ◽  
Automated Image Analysis ◽  
Fine Aggregate ◽  
Smoothing Effect ◽  
Aggregate Properties ◽  
Wide Range

Angularity is one of the important aggregate properties contributing to the permanent deformation resistance of asphalt mixtures. Therefore, methods that are able to rapidly and accurately describe aggregate angularity are valuable in the design process of asphalt mixtures. Two computer-automated procedures, which make use of the advances in digital-image processing, to quantify fine aggregate angularity, are presented. The first method relies on the concepts of the erosion-dilation techniques. This consists of subjecting the aggregate surface to a smoothing effect that causes the angularity elements to disappear from the image. Then, the area lost as a result of the smoothing effect is calculated and used to quantify angularity. The second method is based on the fractal approach. Image-analysis techniques are used to measure the fractal length of aggregate boundary. The fractal length increases with aggregate angularity. The proposed imaging techniques are used to capture the aggregate angularity of 23 sand samples that represent a wide range of materials. The results are compared with visual analysis and indirect methods of measuring fine-aggregate angularity, such as the uncompacted air voids, and the angle of internal friction of aggregate mass. In general, the results indicate much promise for measuring aggregate properties using automated imaging technologies.

scholarly journals Automated Image Analysis to Map the Extent of Deep-Sea Mining Plumes Reveals a Larger Impacted Area Compared to Manual Analysis

2021 ◽  
Author(s):  
Timm Schoening ◽  
Yasemin Bodur ◽  
Kevin Köser
Keyword(s):  
Image Analysis ◽  
Deep Sea ◽  
Image Annotation ◽  
Low Cost ◽  
Image Data ◽  
Sediment Traps ◽  
Automated Image Analysis ◽  
Potential Hazard ◽  
Seabed Sediment ◽  
Cascading Effects

Abstract Deep sea mining for poly-metallic nodules impacts the environment in many ways. A key potential hazard is the creation of a sediment plume from resuspending sediment during seabed mining. The resuspended matter disperses with currents but eventually resettles on the seabed. Resettling causes a blanketing of the seafloor environment, potentially causing harm to in-, epi- and hyperbenthic communities with possible cascading effects into food webs of deep sea habitats. Mapping the extent of such blanketing is thus an important factor in quantifying potential impacts of deep-sea mining.One technology that can assess seabed blanketing is optical imaging with cameras at square-kilometre scale. To efficiently analyse the resulting Terabytes of image data with minimized bias, automated image analysis is required. Moreover, effective quantitative monitoring of the blanketing requires ground truthing of the image data. Here, we present results from a camera-based monitoring of a deep-sea mining simulation combined with automated image analysis using the CoMoNoD method and low-cost seabed sediment traps for quantification of the blanketing thickness. We found that the impacted area was about 50 percent larger than previously determined by manual image annotation.

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.

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