Dataset Information

Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension.

ABSTRACT: Previous methods to measure protozoan numbers mostly rely on manual counting, which suffers from high variation and poor efficiency. Although advanced counting devices are available, the specialized and usually expensive machinery precludes their prevalent utilization in the regular laboratory routine. In this study, we established the ImageJ-based workflow to quantify ciliate numbers in a high-throughput manner. We conducted Tetrahymena number measurement using five different methods: particle analyzer method (PAM), find maxima method (FMM), trainable WEKA segmentation method (TWS), watershed segmentation method (WSM) and StarDist method (SDM), and compared their results with the data obtained from the manual counting. Among the five methods tested, all of them could yield decent results, but the deep-learning-based SDM displayed the best performance for Tetrahymena cell counting. The optimized methods reported in this paper provide scientists with a convenient tool to perform cell counting for Tetrahymena ecotoxicity assessment.

SUBMITTER: Kurnia KA

PROVIDER: S-EPMC9181243 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Performance Comparison of Five Methods for <i>Tetrahymena</i> Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension.

Kurnia Kevin Adi KA Sampurna Bonifasius Putera BP Audira Gilbert G Juniardi Stevhen S Vasquez Ross D RD Roldan Marri Jmelou M MJM Tsao Che-Chia CC Hsiao Chung-Der CD

International journal of molecular sciences 20220526 11

Previous methods to measure protozoan numbers mostly rely on manual counting, which suffers from high variation and poor efficiency. Although advanced counting devices are available, the specialized and usually expensive machinery precludes their prevalent utilization in the regular laboratory routine. In this study, we established the ImageJ-based workflow to quantify ciliate numbers in a high-throughput manner. We conducted <i>Tetrahymena</i> number measurement using five different methods: pa ...[more]

PMID: 35682689

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Project description:Open-source software tools are often used for analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a global community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life-cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.

Project description:IntroductionInterventions that modify the built environment can increase population physical activity levels and prevent chronic disease. The national Cooperative Extension System is poised to implement built environment approaches (i.e., pedestrian/bicycle infrastructure and enhanced access to physical activity spaces), but implementation strategies (i.e., methods or techniques to move research to practice) are needed to improve uptake. Effective implementation strategies address relevant barriers and capitalize on facilitators. The purpose of this study was to understand 1) barriers and facilitators to implementing built environment approaches in two state Extension systems, 2) preferences for built environment approaches, and 3) preferences for implementation strategies.MethodsA cross-sectional online survey was used to understand Extension personnel's preferences for and barriers and facilitators to built environment approaches through a mixed-methods study design. This work was informed by anthropological inquiry as the overall research philosophy, and by the Health Impact Pyramid, Leeman et al.'s classification of implementation strategies, and the Consolidated Framework for Implementation Research as the theoretical frameworks. The survey was distributed to eligible Extension personnel (n = 42) in two states. Quantitative data analysis consisted of numbers/proportions and Friedman tests. Qualitative analysis was completed through a rapid deductive approach to quickly produce actionable results.ResultsFourteen respondents (33%) completed the survey. Most had not implemented physical activity interventions in their communities or had implemented only individual-level interventions, though were interested in implementing built environment approaches. Benches, playground improvements, and crosswalks were the most desired approaches, while facilitation, assessing community strengths and needs, and technical assistance were desired implementation strategies. The most common barriers were relative priority and available resources; facilitators were external policy and incentives and implementation climate.DiscussionExtension personnel are receptive to built environment approaches and engaged with community coalitions. Yet, invested parties prefer individual-level interventions, and agents perceive a lack of resources for implementation. Implementation strategies that build capacity in both the Extension system and community coalitions may address these barriers through increasing relative priority and sharing existing resources. This work is a first step toward compiling implementation strategies to address relevant barriers to built environment approaches in community settings.

Project description:BackgroundDuring development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity investigators must often manually measure cell orientations, which is a time-consuming endeavor.MethodologyTo automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA). PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. Inner ear sensory epithelia including cochleae (P4) and utricles (E17.5) from mice were immunostained for βII-spectrin and imaged on a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear angle measurements for accuracy using a 1-5 agreement scale. For utricle samples, we directly compared PCPA derived measurements against manually derived angle measurements using concordance correlation coefficients (CCC) and Bland-Altman limits of agreement. Finally, PCPA was tested against a variety of images copied from publications examining PCP in various tissues and across various species.ResultsPCPA was able to recognize and count 99.81% of cochlear hair cells (n = 1,1541 hair cells) in a sample set, and was able to obtain ideally accurate planar cell polarity measurements for over 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to >98%. When manual angle measurements for E17.5 utricles were compared, PCPA's measurements fell within -9 to +10 degrees of manually obtained mean angle measures with a CCC of 0.999. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.

Dataset Information

Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension.

Publications

Performance Comparison of Five Methods for <i>Tetrahymena</i> Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension.

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