A Survey of Visualization for Live Cell Imaging

Pretorius, A.J.; Khan, Imtiaz; Errington, Rachel J.

doi:10.1111/cgf.12784

Cited by 18 publications

(18 citation statements)

References 81 publications

(392 reference statements)

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“…Thus, from the systems review [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] we see that these software products contain summary statistical data on quantitative indicators. They focused on providing only key parameters of the education system and do not allow in-depth analysis.…”

Section: Literature and Systems Reviewmentioning

confidence: 99%

Applied research of data management in the education system for decision-making on the example of Al-Farabi Kazakh National University

et al. 2020

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In the context of the university digital transformation, the development of digitalization processes, it is necessary to introduce a data management culture. In most cases, this approach does not allow to see a big picture, as well as to identify “bottlenecks”. There is a need to develop a unified data management strategy, which will assess the value of IT technology, and ensure the adoption of effective decisions. This article highlights the conceptual issues of data management strategies in higher education, using the example of the university. The place of the data processing strategy within the university’s information space is considered a map model for analyzing key issues for decision making. The basic principles of organizing a data strategy for the university are determined. There is also considered an approach to designing and implementing an information and analytical system on the Microsoft Power BI cloud-based business analysis platform, which integrates all disparate data from university’s corporate information systems and transactional data sources. The analysis of reports of key business processes of the university is carried out, in which the formulation of research questions is shown, and there have been given conclusions to these issues. Introduction of the information and analytical system in the contour of the university’s corporate information system will contribute to the early identification of bottlenecks in the organization of the educational system and prompt decision-making in a systematic and comprehensive analysis of the main business processes of the educational organization through building a data map and visualizing all statistical reports.

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Section: Literature and Systems Reviewmentioning

confidence: 99%

Applied research of data management in the education system for decision-making on the example of Al-Farabi Kazakh National University

et al. 2020

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“…To date, most of the interactive tasks that are supported by visualization tools allow users to perform simple visual representation manipulations (e.g., panning and zooming) and selection of model choices (e.g., selecting Naïve-Bayes or Support Vector Machine technique). In a recent survey of biomedical visualizations, it was observed that tools typically offer interactions like rotating and zooming but provide limited support for querying and other more advanced interactive tasks [8]. Ko et al conducted a survey of visualizations for financial data and noted that most visualizations failed to support tasks such as exploring, annotating, and linking [16].…”

Section: Interactionmentioning

confidence: 99%

“…Interactive visualizations, when properly designed, can provide a means for analyzing and exploring large sets of data. Interactive visualizations can help maintain context during exploration, support the identification of patterns, and facilitate a wide variety of tasks in which users engage [8,9]. When involved in data-driven efforts, the tasks that users perform are mostly non-routine, exploratory, and interrelated [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Allowing users to reveal data gradually within a visualization has been shown to be an effective way of aiding analysts to explore and understanding large, multivariate datasets [15]. To date, much of the interaction available in visualizations allows users to perform simple manipulations and selections of model choices [8,16]. This is unfortunate, as researchers note that in addition to allowing users to control which subset of data be visualized, interactions need to enable users to analyze and view data from different perspectives (e.g., change visualizations' density, complexity, configuration, and so on) or add their own inferences (e.g., annotate visualizations) [11,17,18].…”

Section: Introductionmentioning

confidence: 99%

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Discourse with Visual Health Data: Design of Human-Data Interaction

Ola

Sedig

2018

MTI

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Previous work has suggested that large repositories of data can revolutionize healthcare activities; however, there remains a disconnection between data collection and its effective usage. The way in which users interact with data strongly impacts their ability to not only complete tasks but also capitalize on the purported benefits of such data. Interactive visualizations can provide a means by which many data-driven tasks can be performed. Recent surveys, however, suggest that many visualizations mostly enable users to perform simple manipulations, thus limiting their ability to complete tasks. Researchers have called for tools that allow for richer discourse with data. Nonetheless, systematic design of human-data interaction for visualization tools is a non-trivial task. It requires taking into consideration a myriad of issues. Creation of visualization tools that incorporate rich human-data discourse would benefit from the use of design frameworks. In this paper, we examine and present a design process that is based on a conceptual human-data interaction framework. We discuss and describe the design of interaction for a visualization tool intended for sensemaking of public health data. We demonstrate the utility of systematic interaction design in two ways. First, we use scenarios to highlight how our design approach supports a rich and meaningful discourse with data. Second, we present results from a study that details how users were able to perform various tasks with health data and learn about global health trends.

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“…The general paradigm for the analysis of such data is centered on the extraction of information from the cell lineage of all visible cells, for example in the studies by Schneider et al ( 2012 ) and Helfrich et al ( 2015 ), which ultimately leads to its visualization, as described in the study by Pretorius et al ( 2016 ). A cell lineage is a sequence of cells that have developed from a common ancestor.…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology for Characterizing Cell Subpopulations in Automated Time-lapse Microscopy

Hattab

Wiesmann

Becker

et al. 2018

Front. Bioeng. Biotechnol.

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Time-lapse imaging of cell colonies in microfluidic chambers provides time series of bioimages, i.e., biomovies. They show the behavior of cells over time under controlled conditions. One of the main remaining bottlenecks in this area of research is the analysis of experimental data and the extraction of cell growth characteristics, such as lineage information. The extraction of the cell line by human observers is time-consuming and error-prone. Previously proposed methods often fail because of their reliance on the accurate detection of a single cell, which is not possible for high density, high diversity of cell shapes and numbers, and high-resolution images with high noise. Our task is to characterize subpopulations in biomovies. In order to shift the analysis of the data from individual cell level to cellular groups with similar fluorescence or even subpopulations, we propose to represent the cells by two new abstractions: the particle and the patch. We use a three-step framework: preprocessing, particle tracking, and construction of the patch lineage. First, preprocessing improves the signal-to-noise ratio and spatially aligns the biomovie frames. Second, cell sampling is performed by assuming particles, which represent a part of a cell, cell or group of contiguous cells in space. Particle analysis includes the following: particle tracking, trajectory linking, filtering, and color information, respectively. Particle tracking consists of following the spatiotemporal position of a particle and gives rise to coherent particle trajectories over time. Typical tracking problems may occur (e.g., appearance or disappearance of cells, spurious artifacts). They are effectively processed using trajectory linking and filtering. Third, the construction of the patch lineage consists in joining particle trajectories that share common attributes (i.e., proximity and fluorescence intensity) and feature common ancestry. This step is based on patch finding, patching trajectory propagation, patch splitting, and patch merging. The main idea is to group together the trajectories of particles in order to gain spatial coherence. The final result of CYCASP is the complete graph of the patch lineage. Finally, the graph encodes the temporal and spatial coherence of the development of cellular colonies. We present results showing a computation time of less than 5 min for biomovies and simulated films. The method, presented here, allowed for the separation of colonies into subpopulations and allowed us to interpret the growth of colonies in a timely manner.

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A Survey of Visualization for Live Cell Imaging

Cited by 18 publications

References 81 publications

Applied research of data management in the education system for decision-making on the example of Al-Farabi Kazakh National University

Applied research of data management in the education system for decision-making on the example of Al-Farabi Kazakh National University

Discourse with Visual Health Data: Design of Human-Data Interaction

A Novel Methodology for Characterizing Cell Subpopulations in Automated Time-lapse Microscopy

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