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 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

Summaries

2018 ◽  
Vol 46 (1) ◽  
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Text Analysis ◽  
English Language ◽  
Automated Analysis ◽  
Supervised Machine Learning ◽  
Automated Text Analysis ◽  
Research Problems ◽  
Dutch Language ◽  
Automated Methods

Damian Trilling & Jelle Boumans Automated analysis of Dutch language-based texts. An overview and research agenda While automated methods of content analysis are increasingly popular in today’s communication research, these methods have hardly been adopted by communication scholars studying texts in Dutch. This essay offers an overview of the possibilities and current limitations of automated text analysis approaches in the context of the Dutch language. Particularly in dictionary-based approaches, research is far less prolific as research on the English language. We divide the most common types of content-analytical research questions into three categories: 1) research problems for which automated methods ought to be used, 2) research problems for which automated methods could be used, and 3) research problems for which automated methods (currently) cannot be used. Finally, we give suggestions for the advancement of automated text analysis approaches for Dutch texts. Keywords: automated content analysis, Dutch, dictionaries, supervised machine learning, unsupervised machine learning

scholarly journals A deep learning and novelty detection framework for rapid phenotyping in high-content screening

2017 ◽  
Author(s):  
Christoph Sommer ◽  
Rudolf Hoefler ◽  
Matthias Samwer ◽  
Daniel W. Gerlich
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Scale ◽  
Novelty Detection ◽  
A Priori ◽  
Mitotic Cell ◽  
Supervised Machine Learning ◽  
High Content Screening ◽  
Data Sets ◽  
User Training

AbstractSupervised machine learning is a powerful and widely used method to analyze high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening.

scholarly journals Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility

Life ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 44
Author(s):  
Max Riekeles ◽  
Janosch Schirmack ◽  
Dirk Schulze-Makuch
Keyword(s):  
Machine Learning ◽  
Brownian Motion ◽  
Solar System ◽  
Learning Algorithms ◽  
Recognition System ◽  
Life Forms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Automated Identification ◽  
Motility Behavior

(1) Background: Future missions to potentially habitable places in the Solar System require biochemistry-independent methods for detecting potential alien life forms. The technology was not advanced enough for onboard machine analysis of microscopic observations to be performed in past missions, but recent increases in computational power make the use of automated in-situ analyses feasible. (2) Methods: Here, we present a semi-automated experimental setup, capable of distinguishing the movement of abiotic particles due to Brownian motion from the motility behavior of the bacteria Pseudoalteromonas haloplanktis, Planococcus halocryophilus, Bacillus subtilis, and Escherichia coli. Supervised machine learning algorithms were also used to specifically identify these species based on their characteristic motility behavior. (3) Results: While we were able to distinguish microbial motility from the abiotic movements due to Brownian motion with an accuracy exceeding 99%, the accuracy of the automated identification rates for the selected species does not exceed 82%. (4) Conclusions: Motility is an excellent biosignature, which can be used as a tool for upcoming life-detection missions. This study serves as the basis for the further development of a microscopic life recognition system for upcoming missions to Mars or the ocean worlds of the outer Solar System.

THE APPLICATION OF PATTERN RECOGNITION AND MACHINE LEARNING TO DETERMINE CEMENT CHANNELING & BOND QUALITY FROM AZIMUTHAL CEMENT BOND LOGS

2021 ◽  
Author(s):  
Andrew Imrie ◽  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Input Data ◽  
Synthetic Data ◽  
Image Data ◽  
Automated Analysis ◽  
Comparison Method ◽  
Automated Classification ◽  
Bond Quality ◽  
Automated Identification

Cement bond log interpretation methods consist of human pattern recognition and evaluation of the quality of the downhole isolation. Typically, a log interpreter compares acquisition data to their predefined classifications of cement bond quality. This paper outlines a complementary technique of intelligent cement evaluation and the implementation of the analysis of cement evaluation data by utilizing automatic pattern matching and machine learning. The proposed method is capable of defining bond quality across multiple distinct subclassification through analysis of image data using pattern recognition. Libraries of real log responses are used as comparisons to input data, and additionally may be supplemented with synthetic data. Using machine learning and image-based pattern recognition, the bond quality is classified into succinct categories to determine the presence of channeling. Successful classifications of the input data can then be added to the libraries, thus improving future analysis through an iterative process. The system uses the outputs of a conventional azimuthal ultrasonic scanning cement evaluation log and 5-ft CBL waveform to conclude a cement bond interpretation. The 5-ft CBL waveform is an optional addition to the processand improves the interpretation. The system searches forsimilarities between the acquisition data and thatcontained in the library. These similarities are comparedto evaluate the bonding. The process is described in two parts: i) image collection and library classification and ii) pattern recognition and interpretation. The former is the process of generating a readable library of reference data from historical cement evaluation logs and laboratory measurements and the latter is the machine learning and comparison method. Example results are shown with good correlations between automated analysis and interpreter analysis. The system is shown to be particularly capable at the automated identification of channeling of varying sizes, something which would be a challenge when using only the scalar curve representation of azimuthal data. Previously published methodologies for automated classification of bond quality typically utilize scaler data whereas this approach utilizes image-based pattern recognition for automated, learning and intelligent cement evaluation (ALICE). A discussion is presented on the limitations and merits of the ALICE process which include quality control, the removal of analyst bias during interpretation, and the fact that such a system will continually improve in accuracy through supervised training.

scholarly journals A deep learning and novelty detection framework for rapid phenotyping in high-content screening

2017 ◽  
Vol 28 (23) ◽  
pp. 3428-3436 ◽  
Author(s):  
Christoph Sommer ◽  
Rudolf Hoefler ◽  
Matthias Samwer ◽  
Daniel W. Gerlich
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Scale ◽  
Novelty Detection ◽  
A Priori ◽  
Mitotic Cell ◽  
Supervised Machine Learning ◽  
High Content Screening ◽  
Data Sets ◽  
User Training

Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening.

scholarly journals Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1531
Author(s):  
Manish Sharma ◽  
Jainendra Tiwari ◽  
Virendra Patel ◽  
U. Rajendra Acharya
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Sleep Disorder ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Movement Behavior ◽  
Subjective Time ◽  
Eeg Signals ◽  
Automated Identification ◽  
Band Filter

A sleep disorder is a medical condition that affects an individual’s regular sleeping pattern and routine, hence negatively affecting the individual’s health. The traditional procedures of identifying sleep disorders by clinicians involve questionnaires and polysomnography (PSG), which are subjective, time-consuming, and inconvenient. Hence, an automated sleep disorder identification is required to overcome these limitations. In the proposed study, we have proposed a method using electroencephalogram (EEG) signals for the automated identification of six sleep disorders, namely insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, rapid eye movement behavior disorder (RBD), periodic leg movement disorder (PLM), and sleep-disordered breathing (SDB). To the best of our belief, this is one of the first studies ever undertaken to identify sleep disorders using EEG signals employing cyclic alternating pattern (CAP) sleep database. After sleep-scoring EEG epochs, we have created eight different data subsets of EEG epochs to develop the proposed model. A novel optimal triplet half-band filter bank (THFB) is used to obtain the subbands of EEG signals. We have extracted Hjorth parameters from subbands of EEG epochs. The selected features are fed to various supervised machine learning algorithms for the automated classification of sleep disorders. Our proposed system has obtained the highest accuracy of 99.2%, 98.2%, 96.2%, 98.3%, 98.8%, and 98.8% for insomnia, narcolepsy, NFLE, PLM, RBD, and SDB classes against normal healthy subjects, respectively, applying ensemble boosted trees classifier. As a result, we have attained the highest accuracy of 91.3% to identify the type of sleep disorder. The proposed method is simple, fast, efficient, and may reduce the challenges faced by medical practitioners during the diagnosis of various sleep disorders accurately in less time at sleep clinics and homes.

scholarly journals Detecting Impact Craters in Planetary Images Using Machine Learning

2012 ◽  
pp. 146-159 ◽  
Author(s):  
T. F. Stepinski ◽  
Wei Ding ◽  
R. Vilalta
Keyword(s):  
Machine Learning ◽  
Automated Analysis ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Detection Algorithms ◽  
Crater Detection ◽  
Learning Techniques ◽  
Digital Elevation ◽  
Very Large Database ◽  
Opening Up

Prompted by crater counts as the only available tool for measuring remotely the relative ages of geologic formations on planets, advances in remote sensing have produced a very large database of high resolution planetary images, opening up an opportunity to survey much more numerous small craters improving the spatial and temporal resolution of stratigraphy. Automating the process of crater detection is key to generate comprehensive surveys of smaller craters. Here, the authors discuss two supervised machine learning techniques for crater detection algorithms (CDA): identification of craters from digital elevation models (also known as range images), and identification of craters from panchromatic images. They present applications of both techniques and demonstrate how such automated analysis has produced new knowledge about planet Mars.

scholarly journals Semi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy

2019 ◽  
Author(s):  
Diego Ulisse Pizzagalli ◽  
Marcus Thelen ◽  
Santiago Fernandez Gonzalez ◽  
Rolf Krause
Keyword(s):  
Machine Learning ◽  
Intravital Microscopy ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Tracking Accuracy ◽  
Manual Curation ◽  
Additional Imaging ◽  
A Cell ◽  
Multiple Cell ◽  
Living Organisms

Abstract2-photon intravital microscopy (2P-IVM) is a key technique to investigate cell migration and cell-to-cell interactions in organs and tissues of living organisms. Focusing on immunology, 2P-IVM allowed recording videos of leukocytes during the immune response, highlighting unprecedented mechanisms of the immune system. However, the automatic analysis of the acquired videos remains challenging and poorly reproducible. In fact, both manual curation of results and tuning of bioimaging software parameters among different experiments, are required. One of the most difficult tasks for a user is transferring to a computer the knowledge on what a cell is and how it should appear with respect to the background, other objects, or other cell types. This is possibly due to the low specificity of acquisition channels which may include multiple cell populations and the presence of similar objects in the background.In this work, we propose a method based on semi-supervised machine learning to facilitate colocalization. In line with recently proposed approaches for pixel classification, the method requires the user to draw some lines on the cells of interest and some line on the other objects/background. These lines embed knowledge, not only on which pixel belongs to a class or which pixel belongs to another class but also on how pixels in the same object are connected. Hence, the proposed method exploits the information from the lines to create an additional imaging channel that is specific for the cells fo interest. The usage of this method increased tracking accuracy on a dataset of challenging 2P-IVM videos of leukocytes. Additionally, it allowed processing multiple samples of the same experiment keeping the same mathematical model.

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