scholarly journals CytoCensus: mapping cell identity and division in tissues and organs using machine learning

2017 ◽  
Author(s):  
Martin Hailstone ◽  
Dominic Waithe ◽  
Tamsin J Samuels ◽  
Lu Yang ◽  
Ita Costello ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Single Cell Analysis ◽  
Time Lapse ◽  
Supervised Machine Learning ◽  
Multidimensional Data ◽  
Cell Detection ◽  
List Type ◽  
Analysis Software ◽  
Image Analysis Software

AbstractA major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D “point- and-click” user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on these datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues.SummaryHailstone et al. develop CytoCensus, a “point-and-click” supervised machine-learning image analysis software to quantitatively identify defined cell classes and divisions from large multidimensional data sets of complex tissues. They demonstrate its utility in analysing challenging developmental phenotypes in living explanted Drosophila larval brains, mammalian embryos and zebrafish organoids. They further show, in comparative tests, a significant improvement in performance over existing easy-to-use image analysis software.HighlightsCytoCensus: machine learning quantitation of cell types in complex 3D tissuesSingle cell analysis of division rates from movies of living Drosophila brains in 3DDiverse applications in the analysis of developing vertebrate tissues and organoidsOutperforms other image analysis software on challenging, low SNR datasets tested

scholarly journals CytoCensus, mapping cell identity and division in tissues and organs using machine learning

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Martin Hailstone ◽  
Dominic Waithe ◽  
Tamsin J Samuels ◽  
Lu Yang ◽  
Ita Costello ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automated Analysis ◽  
Time Lapse ◽  
Supervised Machine Learning ◽  
Cell Detection ◽  
Automated Identification ◽  
User Training ◽  
General Utility ◽  
Multiple Cell ◽  
Mutant Phenotypes

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D ‘point-and-click’ user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues.

scholarly journals Machine Learning Takes Laboratory Automation to the Next Level

2020 ◽  
Vol 58 (4) ◽  
Author(s):  
Bradley A. Ford ◽  
Erin McElvania
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Clinical Microbiology ◽  
Clinical Laboratory ◽  
Automated Image Analysis ◽  
Laboratory Automation ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Specimen Type ◽  
Link Type

ABSTRACT Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the Journal of Clinical Microbiology, M. L. Faron, B. W. Buchan, R. F. Relich, J. Clark, and N. A. Ledeboer (J Clin Microbiol 58:e01683-19, 2020, https://doi.org/10.1128/JCM.01683-19) evaluate the performance of automated image analysis software to screen urine cultures for further workup according to their total number of CFU. Urine cultures are the highest volume specimen type for most laboratories, so this software has the potential for tremendous gains in laboratory efficiency and quality due to the consistency of colony quantification.

scholarly journals Experimental system and image analysis software for high throughput phenotyping of mycorrhizal growth response in Brachypodium distachyon

2019 ◽  
Author(s):  
Felicia Maviane-Macia ◽  
Camille Ribeyre ◽  
Luis Buendia ◽  
Mégane Gaston ◽  
Mehdi Khafif ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Growth Response ◽  
Brachypodium Distachyon ◽  
Am Fungi ◽  
Plant Phenotyping ◽  
List Type ◽  
Analysis Software ◽  
Nitrogen And Phosphorus ◽  
Image Analysis Software

AbstractPlant growth response to Arbuscular Mycorrhizal (AM) fungi is variable and depends on genetic and environment factors that still remain largely unknown. Identification of these factors can be envisaged using high-throughput and accurate plant phenotyping.We setup experimental conditions based on a two-compartment system allowing to measure Brachypodium distachyon mycorhizal growth response (MGR) in an automated phenotyping greenhouse. We developed a new image analysis software “IPSO Phen” to estimate of B. distachyon aboveground biomass.We found a positive MGR in the B. distachyon Bd3-1 genotype inoculated with the AM fungi Rhizophagus irregularis only if nitrogen and phosphorus were added together in the compartment restricted to AM fungi. Using this condition, we found genetic diversity in B. distachyon for MGR ranging from positive to negative MGR depending on the plant genotype tested.Our result on the interaction between nitrogen and phosphorus for MGR in B. distachyon opens new perspectives about AM functioning. In addition, our open-source software allowing to test and run image analysis parameters on large amount of images generated by automated plant phenotyping facilities, will help to screen large panels of genotypes and environmental conditions to identify the factors controlling the MGR.

Objective detection of apoptosis in rat renal tissue sections using light microscopy and free image analysis software with subsequent machine learning

2017 ◽  
Vol 49 (1) ◽  
pp. 22-27 ◽  
Author(s):  
Nayana Damiani Macedo ◽  
Aline Rodrigues Buzin ◽  
Isabela Bastos Binotti Abreu de Araujo ◽  
Breno Valentim Nogueira ◽  
Tadeu Uggere de Andrade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Light Microscopy ◽  
Renal Tissue ◽  
Analysis Software ◽  
Tissue Sections ◽  
Image Analysis Software ◽  
Objective Detection

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.

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