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Classification of dead and living microalgae Chlorella vulgaris by bioimage informatics and machine learning

ALGAL RESEARCH-BIOMASS BIOFUELS AND BIOPRODUCTS 2020年 48卷

作者： Reimann, Ronny Zeng, Bo Jakopec, Martin Burdukiewicz, Michal Petrick, Ingolf Schierack, Peter Roediger, Stefan Friedrich Alexander Univ Erlangen Nuremberg Biol Dept Div Mol Plant Physiol Erlangen Germany Brandenburg Univ Technol Cottbus Senftenberg Fac Environm & Nat Sci Senftenberg Germany Brandenburg Univ Technol Cottbus Senftenberg Brandenburg Med Sch Theodor Fontane Fac Hlth Sci Joint Fac Senftenberg Germany Univ Potsdam Potsdam Germany Warsaw Univ Technol Fac Math & Informat Sci Warsaw Poland

The ratio between living and dead cells is an important parameter in microalgae culture and environmental monitoring. Fast, robust and automated analytical methods for monitoring microalgae growth for biotechnological and pharmaceutical applications and for optimizing production strains are needed. We developed a pipeline for the automatic binary classification of living and dead microalgae. We compared multispectral fluorescence microscopy and flow cytometry as readout platforms. Images of Chlorella vulgaris suspension cultures were captured and features of microalgaes (e.g., size) were extracted by bioimage informatics. We classified the microalgae cells with seven machine learning algorithms, including Naive Bayes, Random Forest and Neural Networks. Random Forest was a particularly suitable method for classifying the living/dead microalgae population, the label-free classifier can reach 86% of the area under the curve (AUC) if only morphological characteristics are used. The AUC rose to 99.6% if the fluorescence signal of Syto 9 (dye for all cells) and Propidium iodide (dye for damaged cells) were added as features, and the prediction accuracy reached 96.6% which is over the accuracy of 95% from the cell viability analysis using the staining method. This means that our classifier is useful as it improves the staining method by correcting 30% of the false positive/negative cells. Instead of only predicting single cells, we developed a Random Forest model to classify the distribution of living and dead microalgae populations using the fingerprint vector features, which gives an overall AUC of 94.5% and accuracy of 82%. We found that dead cells are significantly larger in average diameter (1.4-fold, p < 0.01) and area (1.5-fold, p < 0.01) when compared to living cells. In summary, combinations of statistical methods, bioimage informatics and machine learning are useful approaches for automated investigation in analyzing population dynamics of microalgae cultures.

关键词： Machine learning Fluorescence microscopy bioimage informatics Flow cytometry Chlorella vulgaris Microalgae

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A bioimage informatics platform for high-throughput embryo phenotyping

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BRIEFINGS IN BIOinformatics 2018年第1期19卷 41-51页

作者： Brown, James M. Horner, Neil R. Lawson, Thomas N. Fiegel, Tanja Greenaway, Simon Morgan, Hugh Ring, Natalie Santos, Luis Sneddon, Duncan Teboul, Lydia Vibert, Jennifer Yaikhom, Gagarine Westerberg, Henrik Mallon, Ann-Marie MRC Harwell Inst Harwell Campus Didcot OX11 0RD Oxon England MRC Harwell Inst Bioimage Informat BioComp Didcot Oxon England MRC Harwell Inst BioComp Didcot Oxon England

High-throughput phenotyping is a cornerstone of numerous functional genomics projects. In recent years, imaging screens have become increasingly important in understanding gene-phenotype relationships in studies of cells, tissues and whole organisms. Three-dimensional (3D) imaging has risen to prominence in the field of developmental biology for its ability to capture whole embryo morphology and gene expression, as exemplified by the International Mouse Phenotyping Consortium (IMPC). Large volumes of image data are being acquired by multiple institutions around the world that encompass a range of modalities, proprietary software and metadata. To facilitate robust downstream analysis, images and metadata must be standardized to account for these differences. As an open scientific enterprise, making the data readily accessible is essential so that members of biomedical and clinical research communities can study the images for themselves without the need for highly specialized software or technical expertise. In this article, we present a platform of software tools that facilitate the upload, analysis and dissemination of 3D images for the IMPC. Over 750 reconstructions from 80 embryonic lethal and subviable lines have been captured to date, all of which are openly accessible at mousephenotype. org. Although designed for the IMPC, all software is available under an open-source licence for others to use and develop further. Ongoing developments aim to increase throughput and improve the analysis and dissemination of image data. Furthermore, we aim to ensure that images are searchable so that users can locate relevant images associated with genes, phenotypes or human diseases of interest.

关键词： bioimage informatics image processing embryonic phenotyping automated analysis high-throughput software tools

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An Overview of data science uses in bioimage informatics

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METHODS 2017年 115卷 110-118页

作者： Chessel, Anatole Univ Paris Saclay CNRS INSERIVI LOBEcole Polytech F-91128 Palaiseau France

This review aims at providing a practical overview of the use of statistical features and associated data science methods in bioimage informatics. To achieve a quantitative link between images and biological concepts, one typically replaces an object coming from an image (a segmented cell or intracellular object, a pattern of expression or localisation, even a whole image) by a vector of numbers. They range from carefully crafted biologically relevant measurements to features learnt through deep neural networks. This replacement allows for the use of practical algorithms for visualisation, comparison and inference, such as the ones from machine learning or multivariate statistics. While originating mainly, for biology, in high content screening, those methods are integral to the use of data science for the quantitative analysis of microscopy images to gain biological insight, and they are sure to gather more interest as the need to make sense of the increasing amount of acquired imaging data grows more pressing. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Data science bioimage informatics High content screening Image analysis

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Colony fingerprinting - a novel method for discrimination of food-contaminating microorganisms based on bioimage informatics

Colony fingerprinting - a novel method for discrimination of...

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Tanaka, Tsuyoshi Kogiso, Atsushi Maeda, Yoshiaki Matsunaga, Tadashi Tokyo Univ Agr & Technol Div Biotechnol & Life Sci 2-24-16 Naka Cho Koganei Tokyo Japan

ISBN: (纸本)9781728103976

Discrimination of food-contaminating microorganisms is an essential technology to secure the safety in manufacturing of foods and beverages. Conventionally, discrimination of the microorganisms has been performed by morphological observation, genetic analysis, and more recently, biochemical fingerprinting using mass spectrometry. However, several drawbacks exist in these methods, such as long assay time, cumbersome operations, and expensive equipment. To address these issues, we have proposed a novel method for discrimination of food-contaminating microorganisms, termed "colony fingerprinting", based on bioimage informatics. In colony fingerprinting, growth of bacterial colonies were monitored using a lens-less imaging system. The characteristic images of colonies, referred to as colony fingerprints (CFPs), were obtained over time, and subsequently used to extract discriminative parameters. We demonstrated to discriminate 20 bacterial species by analyzing the extracted parameters with machine learning approaches, namely support vector machine and random forest. Colony fingerprinting is a promising method for rapid and easy discrimination of food-contaminating microorganisms.

关键词： colony fingerprinting food-contaminating microorganisms bioimage informatics lens-less imaging machine learning

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bioimage-based protein subcellular location prediction： a comprehensive review

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Frontiers of Computer Science 2018年第1期12卷 26-39页

作者： Ying-Ying XU Li-Xiu YAO Hong-Bin SHEN Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location pat- terns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic char- acteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systemati- cally reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and clas- sified them into four categories including growing of bioim- age databases, description of subcellular location distribution patterns, classification methods, and applications of the pre- diction systems. Besides, we also discussed some potential directions in this field.

关键词： bioimage informatics protein subcellular loca-tion prediction global and local features multi-location pro-tein recognition

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Cell Detection From Redundant Candidate Regions Under Nonoverlapping Constraints

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2015年第7期34卷 1417-1427页

作者： Bise, Ryoma Sato, Yoichi Univ Tokyo Inst Ind Sci Tokyo 1538505 Japan Dai Nippon Printing Tokyo 1158001 Japan

Cell detection in microscopy images is essential for automated cell behavior analysis including cell shape analysis and cell tracking. Robust cell detection in high-density and low-contrast images is still challenging since cells often touch and partially overlap, forming a cell cluster with blurry intercellular boundaries. In such cases, current methods tend to detect multiple cells as a cluster. If the control parameters are adjusted to separate the touching cells, other problems often occur: a single cell may be segmented into several regions, and cells in low-intensity regions may not be detected. To solve these problems, we first detect redundant candidate regions, which include many false positives but in turn very few false negatives, by allowing candidate regions to overlap with each other. Next, the score for how likely the candidate region contains the main part of a single cell is computed for each cell candidate using supervised learning. Then we select an optimal set of cell regions from the redundant regions under nonoverlapping constraints, where each selected region looks like a single cell and the selected regions do not overlap. We formulate this problem of optimal region selection as a binary linear programming problem under nonoverlapping constraints. We demonstrated the effectiveness of our method for several types of cells in microscopy images. Our method performed better than five representative methods, achieving an F-measure of over 0.9 for all data sets. Experimental application of the proposed method to 3-D images demonstrated that also works well for 3-D cell detection.

关键词： bioimage informatics cell detection microscopy image

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GroupTracker: Video tracking system for multiple animals under severe occlusion

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2015年 57卷 39-45页

作者： Fukunaga, Tsukasa Kubota, Shoko Oda, Shoji Iwasaki, Wataru Univ Tokyo Grad Sch Frontier Sci Dept Computat Biol Chiba 2778568 Japan Univ Tokyo Atmosphere & Ocean Res Inst Chiba 2778564 Japan Univ Tokyo Grad Sch Frontier Sci Dept Integrated Biosci Chiba 2778562 Japan Univ Tokyo Grad Sch Sci Dept Biol Sci Tokyo 1130032 Japan

Quantitative analysis of behaviors shown by interacting multiple animals can provide a key for revealing high-order functions of their nervous systems. To resolve these complex behaviors, a video tracking system that preserves individual identity even under severe overlap in positions, i.e., occlusion, is needed. We developed GroupTracker, a multiple animal tracking system that accurately tracks individuals even under severe occlusion. As maximum likelihood estimation of Gaussian mixture model whose components can severely overlap is theoretically an ill-posed problem, we devised an expectation maximization scheme with additional constraints on the eigenvalues of the covariance matrix of the mixture components. Our system was shown to accurately track multiple medaka (Oryzias Iatipes) which freely swim around in three dimensions and frequently overlap each other. As an accurate multiple animal tracking system, GroupTracker will contribute to revealing unexplored structures and patterns behind animal interactions. The Java source code of GroupTracker is available at https://***/site/fukunagatsuisoftwareigroup-tracker. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： bioimage informatics Computational ethology Animal tracking

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Integrating semantic annotation and information visualization for the analysis of multichannel fluorescence micrographs from pancreatic tissue

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2010年第6期34卷 446-452页

作者： Herold, Julia Zhou, Luxian Abouna, Sylvie Pelengaris, Stella Epstein, David Khan, Michael Nattkemper, Tim W. Univ Bielefeld Biodata Min & Appl Neuroinformat Grp D-33615 Bielefeld Germany Univ Warwick Dept Biol Sci Biomed Res Inst Coventry CV4 7AL W Midlands England Univ Warwick Sch Med Coventry CV4 7AL W Midlands England Univ Warwick Math Inst Coventry CV4 7AL W Midlands England

The challenging problem of computational bioimage analysis receives growing attention from life sciences. Fluorescence microscopy is capable of simultaneously visualizing multiple molecules by staining with different fluorescent dyes. In the analysis of the result multichannel images, segmentation of ROIs resembles only a first step which must be followed by a second step towards the analysis of the ROI's signals in the different channels. In this paper we present a system that combines image segmentation and information visualization principles for an integrated analysis of fluorescence micrographs of tissue samples. The analysis aims at the detection and annotation of cells of the Islets of Langerhans and the whole pancreas, which is of great importance in diabetes studies and in the search for new anti-diabetes treatments. The system operates with two modules. The automatic annotation module applies supervised machine learning for cell detection and segmentation. The second information visualization module can be used for an interactive classification and visualization of cell types following the link-and-brush principle for filtering. We can compare the results obtained with our system with results obtained manually by an expert, who evaluated a set of example images three times to account for his intra-observer variance. The comparison shows that using our system the images can be evaluated with high accuracy which allows a considerable speed up of the time-consuming evaluation process. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Fluorescence microscopy bioimage informatics Image processing Pattern recognition Machine vision Information visualization Exploratory data analysis (EDA) alpha- and beta-cell counting

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Automated quantification of dopaminergic immunostained neurons in substantia nigra using freely available software

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第10期60卷 2995-3007页

作者： Bonaccorso Marinelli, Maria Paula Baiardi, Gustavo Ruth Valdez, Susana Jorge Cabrera, Ricardo Univ Mendoza Inst Invest Biomed INBIOMED Huarpes 630 RA-5500 Mendoza Argentina Univ Nacl Cuyo Inst Med & Biol Expt Cuyo IMBECU CONICET Av Dr Adrian Ruiz Leal RA-5500 Mendoza Argentina Univ Mendoza Inst Bioingn Fac Ingn Av Boulogne Sur Mer 683 RA-5500 Mendoza Argentina Univ Catolica Cordoba Univ Nacl Cordoba Fac Ciencias Quim Lab NeurofarmacolIIBYT CONICET Cordoba Argentina Univ Nacl Cuyo Inst Med & Biol Expt Cuyo IMBECU CONICET Lab Reprod & Lactancia Av Dr Adrian Ruiz Leal RA-5500 Mendoza Argentina Univ Nacl Cuyo Fac Ciencias Exactas & Nat Padre J Contreras 1300 RA-5500 Mendoza Argentina

Computerized techniques for image analysis are critical for progress in cell biology. The complexity of the data in current methods eliminates the need for manual image analysis and usually requires the application of multiple algorithms sequentially to the images. Our aim was to develop a software for immunohistochemical analysis of brain dopaminergic neurons combining several computational approaches to automatically analyze and quantify their number in the substantia nigra after a neurotoxic injury. For this purpose, we used a Parkinson's disease animal model to test our application. The dopaminergic neurotoxin, 6-hydroxydopamine, was administered in adult male rats to damage dopaminergic neurons in substantia nigra and to induce hemiparkinsonism. The lesion was corroborated by behavioral evaluation in response to apomorphine and amphetamine. The animals were euthanized and their brains processed for tyrosine hydroxylase immunohistochemistry for dopamine neuron identification. Neurons positive for tyrosine hydroxylase were evaluated in substantia nigra by light microscopy. The images were used to show quantification applicability. To test our software counting accuracy and validity, automatic dopamine neuron number was correlated with the data obtained by three independent observers. Several parameters were used to depict neuronal function in dataset images from control and lesioned brains. In conclusion, we could perform an automated quantification of dopaminergic neurons and corroborate the validity and accuracy of a freely available software.

关键词： Image analysis bioimage informatics Hemiparkinsonism 6-OHDA Cell count Validity test

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Contrast-Independent Curvilinear Structure Detection in Biomedical Images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2012年第5期21卷 2572-2581页

作者： Obara, Boguslaw Fricker, Mark Gavaghan, David Grau, Vicente Univ Oxford Oxford E Res Ctr Oxford OX1 3QG England Oxford Ctr Integrat Syst Biol Oxford OX1 3QG England Univ Oxford Dept Plant Sci Oxford OX1 3RB England Oxford Ctr Integrat Syst Biol Dept Comp Sci Oxford OX1 3QU England Univ Oxford Inst Biomed Engn Dept Engn Sci Oxford OX1 3QG England

Many biomedical applications require detection of curvilinear structures in images and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here, we propose a contrast-independent approach to identify curvilinear structures based on oriented phase congruency, i.e., the phase congruency tensor (PCT). We show that the proposed method is largely insensitive to intensity variations along the curve and provides successful detection within noisy regions. The performance of the PCT is evaluated by comparing it with state-of-the-art intensity-based approaches on both synthetic and real biological images.

关键词： bioimage informatics curvilinear structure live-wire tracing phase congruency tensor (PCT)

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