Recent Trends and Perspectives in Cerebral Organoids Imaging and Analysis

Purpose: Since their first generation in 2013, the use of cerebral organoids has spread exponentially. Today, the amount of generated data is becoming challenging to analyze manually. This review aims to overview the current image acquisition methods and to subsequently identify the needs in image analysis tools for cerebral organoids.Methods: To address this question, we went through all recent articles published on the subject and annotated the protocols, acquisition methods, and algorithms used.Results: Over the investigated period of time, confocal microscopy and bright-field microscopy were the most used acquisition techniques. Cell counting, the most common task, is performed in 20% of the articles and area; around 12% of articles calculate morphological parameters. Image analysis on cerebral organoids is performed in majority using ImageJ software (around 52%) and Matlab language (4%). Treatments remain mostly semi-automatic. We highlight the limitations encountered in image analysis in the cerebral organoid field and suggest possible solutions and implementations to develop.Conclusions: In addition to providing an overview of cerebral organoids cultures and imaging, this work highlights the need to improve the existing image analysis methods for such images and the need for specific analysis tools. These solutions could specifically help to monitor the growth of future standardized cerebral organoids.

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Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100139627 ◽

1995 ◽

Vol 53 ◽

pp. 650-651

Author(s):

D. E. Becker

Keyword(s):

Image Analysis ◽

High Resolution ◽

Laser Scanning ◽

Cell Counting ◽

Wide Area ◽

Data Set ◽

Computational Synthesis ◽

Automated Cell Counting ◽

Partial View ◽

The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

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Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100163873 ◽

1996 ◽

Vol 54 ◽

pp. 282-283

Author(s):

Badrinath Roysam ◽

Hakan Ancin ◽

Douglas E. Becker ◽

Robert W. Mackin ◽

Matthew M. Chestnut ◽

...

Keyword(s):

Image Analysis ◽

Structural Changes ◽

Sampling Methods ◽

Spatial Clustering ◽

Three Dimensional ◽

Field Of View ◽

Cell Counting ◽

Depth Of Field ◽

Tissue Cell ◽

Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

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Application of AFP Nanoprobing Light-Induced Photovoltaic Current in the Failure Analysis

ISTFA 2017: Conference Proceedings from the 43rd International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2017p0427 ◽

2017 ◽

Author(s):

C.Q. Chen ◽

P.T. Ng ◽

G.B. Ang ◽

Francis Rivai ◽

S.L. Ting ◽

...

Keyword(s):

Image Analysis ◽

Failure Analysis ◽

Photovoltaic Effect ◽

Electrical Characterization ◽

Fault Isolation ◽

Device Performance ◽

Semiconductor Technology ◽

Current Image ◽

Scaling Down ◽

Detailed Circuit

Abstract As semiconductor technology keeps scaling down, failure analysis and device characterizations become more and more challenging. Global fault isolation without detailed circuit information comprises the majority of foundry EFA cases. Certain suspected areas can be isolated, but further narrow-down of transistor and device performance is very important with regards to process monitoring and failure analysis. A nanoprobing methodology is widely applied in advanced failure analysis, especially during device level electrical characterization. It is useful to verify device performance and to prove the problematic structure electrically. But sometimes the EFA spot coverage is too big to do nanoprobing analysis. Then further narrow-down is quite critical to identify the suspected structure before nanoprobing is employed. That means there is a gap between global fault isolation and localized device analysis. Under these kinds of situation, PVC and AFP current image are offen options to identify the suspected structure, but they still have their limitation for many soft defect or marginal fails. As in this case, PVC and AFP current image failed to identify the defect in the spot range. To overcome the shortage of PVC and AFP current image analysis, laser was innovatively applied in our current image analysis in this paper. As is known to all, proper wavelength laser can induce the photovoltaic effect in the device. The photovoltaic effect induced photo current can bring with it some information of the device. If this kind of information was properly interpreted, it can give us some clue of the device performance.

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Leveraging Automated Image Analysis Tools to Transform Our Capacity to Assess Status and Trends of Coral Reefs

Frontiers in Marine Science ◽

10.3389/fmars.2019.00222 ◽

2019 ◽

Vol 6 ◽

Cited By ~ 13

Author(s):

Ivor D. Williams ◽

Courtney S. Couch ◽

Oscar Beijbom ◽

Thomas A. Oliver ◽

Bernardo Vargas-Angel ◽

...

Keyword(s):

Image Analysis ◽

Coral Reefs ◽

Automated Image Analysis ◽

Analysis Tools ◽

Status And Trends

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Evaluation of ImageJ software in ultrasonic image analysis: follicular and luteal morphological characteristics of cattle

Animal Reproduction Science ◽

10.1016/j.anireprosci.2021.106907 ◽

2021 ◽

pp. 106907

Author(s):

Lindomar Sousa Brito ◽

Ana Karina da Silva Cavalcante ◽

Alexandra Soares Rodrigues ◽

Priscila Assis Ferraz ◽

Rodrigo Freitas Bittencourt ◽

...

Keyword(s):

Image Analysis ◽

Morphological Characteristics ◽

Ultrasonic Image ◽

Imagej Software

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Considerations for Whole-Slide Analysis of Murine Xenografts Experiments

Journal of Histochemistry & Cytochemistry ◽

10.1369/00221554211046994 ◽

2021 ◽

Vol 69 (10) ◽

pp. 627-631

Author(s):

Abigail R. Bland ◽

John C. Ashton

Keyword(s):

Cell Death ◽

Image Analysis ◽

Cell Counting ◽

Analysis Techniques ◽

The Core ◽

Necrotic Region ◽

Areas Of Interest ◽

Image Analysis Techniques ◽

Immunohistochemical Analyses ◽

Animal Models Of Cancer

Histochemistry of tumor sections is a widely employed technique utilized to examine cell death in preclinical xenograft animal models of cancer. However, this is under the assumption that tumors are homogeneous, leading to practices such as automatic cell counting across the entire section. We have noted that in our experiments the core of the tumor is largely or partially necrotic, and lacks evidence of vascularization (in contrast to the outer areas of the tumor). We note that this can bias and confound immunohistochemical analyses that do not take care to sample areas of interest in a way to take this into account. Design-based stereology with image analysis techniques is an alternative process that could be used to measure the volume of the necrotic region compared to the volume of the whole tumor.

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The Image Analysis, Tools for Measuring Quality Criteria of Apples by Characterization of its Cellular Structure

Asian Journal of Applied Sciences ◽

10.3923/ajaps.2011.286.296 ◽

2011 ◽

Vol 4 (3) ◽

pp. 286-296 ◽

Cited By ~ 3

Author(s):

S. Ouattara ◽

G. Loum ◽

A. Clement

Keyword(s):

Image Analysis ◽

Cellular Structure ◽

Quality Criteria ◽

Analysis Tools

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Quantification of gap junctional intercellular communication based on digital image analysis

AJP Regulatory Integrative and Comparative Physiology ◽

10.1152/ajpregu.00089.2009 ◽

2009 ◽

Vol 297 (2) ◽

pp. R243-R247 ◽

Cited By ~ 5

Author(s):

Johannes P. Hofgaard ◽

Sarah Mollerup ◽

Niels-Henrik Holstein-Rathlou ◽

Morten Schak Nielsen

Keyword(s):

Image Analysis ◽

Digital Image ◽

Intercellular Communication ◽

Connexin 43 ◽

Digital Image Analysis ◽

Fluorescent Dyes ◽

Cell Counting ◽

C6 Glioma Cells ◽

Gap Junctional Intercellular Communication ◽

Metabolic Coupling

Intercellular communication via gap junction channels can be quantified by several methods based on diffusion of fluorescent dyes or metabolites. Given the variation in intercellular coupling of cells, even under untreated control conditions, it is of essence to quantify the coupling between numerous cells to obtain reliable estimates of metabolic coupling. Quantification is often based on manual counting of fluorescent cells, which is time consuming and may include some degree of subjectivity. In this report, we introduce a technique based on digital image analysis, and the software for the analysis is presented together with a detailed protocol in the online supplemental material ( http://bmi.ku.dk/matlab_program/ ). Fluorescent dye was introduced in connexin 43-expressing C6 glioma cells by in situ electroporation, and fluorescence intensity was measured in the electroporated cells and in cells receiving dye by intercellular diffusion. The analysis performed is semiautomatic, and comparison with traditional cell counting shows that this method reliably determines the effect of uncoupling by several interventions. This new method of analysis yields a rapid and objective quantification process with a high degree of reproducibility.

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The expansion of TRIAC to TRIACII code for track measurements from SSNT detectors

HNPS Proceedings ◽

10.12681/hnps.2636 ◽

2020 ◽

Vol 15 ◽

pp. 180

Author(s):

D. L. Patiris ◽

K. Blekas ◽

K. G. Ioannides

Keyword(s):

Image Analysis ◽

Solid State ◽

Hough Transform ◽

Alpha Particles ◽

Mode I ◽

Mode Ii ◽

Mean Intensity ◽

Nuclear Track Detectors ◽

Analysis Tools ◽

Energy Discrimination

The expansion of TRIAC to TRIACII code will be described. Both codes have been developed for recognition and parameters measurements of particles’ tracks from images of Solid State Nuclear Track Detectors. While the first program considers the tracks as circles, TRIACII code, using image analysis tools, counts the number of tracks and depending on the current working mode classifies them according to their radii (Mode I- circular tracks) or their axis (Mode II- elliptical tracks), their mean intensity value (brightness) and their orientation. Hough transform techniques are used for the estimation of tracks’ number and their parameters which are able to give results even for overlapping tracks. The new program has been used for radon’s progeny behavior and alpha particles’ energy discrimination.

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