scholarly journals BacStalk: A comprehensive and interactive image analysis software tool for bacterial cell biology

2020 ◽  
Vol 114 (1) ◽  
pp. 140-150 ◽  
Author(s):  
Raimo Hartmann ◽  
Muriel C. F. Teeseling ◽  
Martin Thanbichler ◽  
Knut Drescher
Keyword(s):  
Image Analysis ◽  
Bacterial Cell ◽  
Cell Biology ◽  
Software Tool ◽  
Analysis Software ◽  
Image Analysis Software

scholarly journals BacStalk: a comprehensive and interactive image analysis software tool for bacterial cell biology

2018 ◽  
Author(s):  
Raimo Hartmann ◽  
Muriel C.F. van Teeseling ◽  
Martin Thanbichler ◽  
Knut Drescher
Keyword(s):  
Image Analysis ◽  
Data Analysis ◽  
Cell Biology ◽  
Software Tool ◽  
Model Organisms ◽  
Analysis Software ◽  
Prokaryotic Cell ◽  
Image Analysis Software ◽  
Spatial Dimensions ◽  
Underlying Mechanisms

ABSTRACTProkaryotes display a remarkable spatiotemporal organization of processes within individual cells. Investigations of the underlying mechanisms rely extensively on the analysis of microscopy images. Advanced image analysis software has revolutionized the cell-biological studies of established model organisms with largely symmetric rod-like cell shapes. However’ algorithms suitable for analyzing features of morphologically more complex model species are lacking’ although such unusually shaped organisms have emerged as treasure-troves of new molecular mechanisms and diversity in prokaryotic cell biology. To address this problem’ we developed BacStalk’ a simple’ interactive’ and easy-to-use MatLab-based software tool for quantitatively analyzing images of commonly and uncommonly shaped bacteria’ including stalked (budding) bacteria. BacStalk automatically detects the separate parts of the cells (cell body’ stalk’ bud’ or appendage) as well as their connections’ thereby allowing in-depth analyses of the organization of morphologically complex bacteria over time. BacStalk features the generation and visualization of concatenated fluorescence profiles along cells’ stalks’ appendages’ and buds to trace the spatiotemporal dynamics of fluorescent markers. Cells are interactively linked to demographs’ kymographs’ cell lineage analyses’ and scatterplots’ which enables intuitive and fast data exploration and’ thus’ significantly speeds up the image analysis process. Furthermore’ BacStalk introduces a 2D representation of demo- and kymographs’ enabling data representations in which the two spatial dimensions of the cell are preserved. The software was developed to handle large data sets and to generate publication-grade figures that can be easily edited. BacStalk therefore provides an advanced image analysis platform that extends the spectrum of model organisms for prokaryotic cell biology to bacteria with multiple morphologies and life cycles.IMPORTANCEProkaryotic cells show a striking degree of subcellular organization. Studies of the underlying mechanisms and their variation among different species greatly enhance our understanding of prokaryotic cell biology. The image analysis software tool BacStalk extracts an unprecedented amount of information from images of stalked bacteria, by generating interactive demographs, kymographs, cell lineages, and scatter plots that aid fast and thorough data analysis and representation. Notably, BacStalk can preserve the two spatial dimensions of cells when generating demographs and kymographs to accurately and intuitively reflect the intracellular organization. BacStalk also performs well on established, non-stalked model organisms with common or uncommon shapes. BacStalk therefore contributes to the advancement of prokaryotic cell biology, as it widens the spectrum of easily accessible model organisms and enables a more intuitive and interactive data analysis and visualization.

Dedicated Image Analysis Software Tool for the Evaluation of the Resorption Activity of Cultured Osteoclasts

2008 ◽  
Vol 52 (3) ◽  
pp. 030508 ◽  
Author(s):  
Stefano Z. M. Brianza ◽  
Patrizia D’Amelio ◽  
Marco Cerrato ◽  
Cristina Bignardi ◽  
Anastasia Grimaldi ◽  
...  
Keyword(s):  
Image Analysis ◽  
Software Tool ◽  
Analysis Software ◽  
Image Analysis Software

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

Image analysis software and sample preparation demands

1990 ◽  
Author(s):  
Karl n. Roth ◽  
Knut Wenzelides ◽  
Guenter Wolf ◽  
Peter Hufnagl
Keyword(s):  
Image Analysis ◽  
Sample Preparation ◽  
Analysis Software ◽  
Image Analysis Software

Estimation of bodyweight from body measurements and determination of body measurements on Limousin cattle using digital image analysis

2016 ◽  
Vol 56 (12) ◽  
pp. 2060 ◽  
Author(s):  
Serkan Ozkaya ◽  
Wojciech Neja ◽  
Sylwia Krezel-Czopek ◽  
Adam Oler
Keyword(s):  
Image Analysis ◽  
Body Length ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Digital Camera ◽  
Body Measurements ◽  
Analysis Software ◽  
Body Area ◽  
Image Analysis Software

The objective of this study was to predict bodyweight and estimate body measurements of Limousin cattle using digital image analysis (DIA). Body measurements including body length, wither height, chest depth, and hip height of cattle were determined both manually (by measurements stick) and by using DIA. Body area was determined by using DIA. The images of Limousin cattle were taken while cattle were standing in a squeeze chute by a digital camera and analysed by image analysis software to obtain body measurements of each animal. While comparing the actual and predicted body measurements, the accuracy was determined as 98% for wither height, 97% for hip height, 94% for chest depth and 90.6% for body length. Regression analysis between body area and bodyweight yielded an equation with R2 of 61.5%. The regression equation, which included all body traits, resulted in an R2 value of 88.7%. The results indicated that DIA can be used for accurate prediction of body measurements and bodyweight of Limousin cattle.

Research on image analysis software for elevator brake wheel monitoring

2021 ◽  
Author(s):  
Shuangchang Feng ◽  
Pengzhao Zhang ◽  
Wenhao Shen ◽  
Pengbo Liu
Keyword(s):  
Image Analysis ◽  
Analysis Software ◽  
Image Analysis Software
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