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IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Uddin, Mostofa Rafid Ahmed, Ajmain Yasar Khan, Kafi Fatemi, Md Shahrar Zeng, Xiangrui Xu, Min Carnegie Mellon Univ Dept Computat Biol Pittsburgh PA 15213 USA Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665424714

Cellular electron cryo-tomography (cryo-ET) has made possible the systematic 3D visualization of the near-native structures and spatial-organizations of large macromolecules (represented as subtomograms) and their interactions with organelles inside single cells. It has emerged as a major tool for in situ structural biology. However, the systematic identification of such macromolecules from cryo-ET images is very difficult due to structural complexity and imaging limits. In particular, conventional methods are too slow to process millions of highly structurally heterogeneous macromolecules fastly imaged using cryo-ET. Since 2017, supervised deep learning has become an important tool for facilitating high-throughput analysis of cryo-ET data. However, supervised learning based approaches depends on manual data annotation by biologists, which is an extremely time-consuming and burdensome process. Therefore, none of these methods are practical to use. In order to facilitate deep learning for practical identification of macromolecules from cryo-ET images, in this paper, we demonstrate the pathway towards unsupervised learning for fast and high-throughput identification of macromolecules from cryo-ET images. To this end, we demonstrate the use of three selected recent macromolecule identification methods on several commonly used benchmark cryo-ET datasets.

关键词： bioimage informatics Image classification Cryo-electron tomography Unsupervised learning

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Data mining and sharing tool for high content screening large scale biological image data 2

Data mining and sharing tool for high content screening larg...

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IEEE International Conference on Big Data

作者： Shah, Asmi H. Gopalakrishnan, Gayathri Rajendran, Adithya Liebel, Urban Harvard Med Sch MGH Boston MA 02115 USA Lets Nurture Inc Ahmadabad Gujarat India Karlsruhe Inst Technol Karlsruhe Germany Acquifer AG Karlsruhe Germany

ISBN: (纸本)9781479956661

The constantly developing high content and high throughput screening (HCS & HTS) microscopy and next generation sequencing technologies routinely produce experiment datasets in the terabyte (TB) or petabyte (PB) range resulting in millions of data files that can vary from simply numbers to signals and images. If the collaborators working on the same project are spread over large geographical distances, data sharing, interactive visualization and collaborative annotation techniques become important determinants of the success of a research project. On the other hand, there are hundreds of bioinformatic and cheminformatic databases, billions of documents in available literature, and many image based biological repositories, which need to be referred simultaneously to make sense out of the acquired data. To draw the conclusions from such increasingly complex and large scale data sources, the scientific community must be provided with simple to use methods to retrieve, analyze, visualize, annotate, and crosslink these data sources on a common platform in an efficient manner. However, on the other hand, these modern biological experiments and the subsequent analyses are completed with use of an array of different software suites and automated tools. A constant feedback from the experimenter is needed to change experimental paradigms for the follow-up experiments. Such a software platform to address the HCS data in these aspects does not exist yet, to the best of our knowledge. We have developed a simple to use software package called "AskMe" for users to publish their large scale biological experiment data on to the web by use of data mining and visualization concepts. With use of AskMe, scientists can share these HCS datasets easily with their collaborators or made publicly accessible to the whole scientific community. From the initial stages of experiments, AskMe can ease the experimental analysis process by mining data and providing useful visualizations. Moreover, in

关键词： large scale data handling high content screening high throughput screening bioimage informatics data mining web bigdata

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Cell Tracking for Cell Image Analysis 3

Cell Tracking for Cell Image Analysis

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3rd Biomedical Imaging and Sensing Conference / Congress on Optics and Photonics International

作者： Bise, Ryoma Sato, Yoichi Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan Univ Tokyo Inst Ind Sci Meguro Ku Tokyo 1158001 Japan

ISBN: (数字)9781510610040

ISBN: (纸本)9781510610033;9781510610040

Cell image analysis is important for research and discovery in biology and medicine. In this paper, we present our cell tracking methods, which is capable of obtaining fine-grain cell behavior metrics. In order to address difficulties under dense culture conditions, where cell detection cannot be done reliably since cell often touch with blurry intercellular boundaries, we proposed two methods which are global data association and jointly solving cell detection and association. We also show the effectiveness of the proposed methods by applying the method to the biological researches.

关键词： cell image analysis bioimage informatics computer vision cell tracking optimization

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Features of DNA Signals Codistribution in High-Content Screening of Drug-Induced Demethylation in Cancer Cells

Features of DNA Signals Codistribution in High-Content Scree...

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3rd International Conference on Information Technologies in Biomedicine (ITIB)

作者： Gertych, Arkadiusz Cedars Sinai Med Ctr Dept Surg Bioinformat Lab Los Angeles CA 90048 USA

ISBN: (纸本)9783642311963

The main aim of this work is to introduce new features for the automated evaluation of fluorescence signals of methylated and global DNA, to quickly and objectively measure changes resulting from a demethylating anti-cancer drug application in cell nuclei. Drug response can be monitored in a high-content fashion by means of three dimensional (3-D) image cytometry. This technique enables extraction of basic DNA signal intensities that can be converted to more sophisticated features such as a codistribution and its derivatives. In this work, a slope of nuclear DNA codistribution patterns extracted from individual cells, and whole cell populations and divergences between DNA codistributions of untreated as well as drug treated cells were analysed. A progressive decrease of the slope and an increase of the divergence observed for low drug concentrations suggest that these features can be essential for high-content screening of agents with demethylating potency.

关键词： high-content screening bioimage informatics DNA methylation demethylating drugs quantitative immunofluorescence

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A Web2.0 Strategy for the Collaborative Analysis of Complex bioimages

A Web2.0 Strategy for the Collaborative Analysis of Complex ...

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10th International Symposium on Intelligent Data Analysis

作者： Loyek, Christian Koelling, Jan Langenkaemper, Daniel Niehaus, Karsten Nattkemper, Tim W. Univ Bielefeld Fac Technol Biodata Min Grp Bielefeld Germany Bielefeld Univ Fac Biol Proteome & Metabolome Res Genome Res & Syst Biol Bielefeld Germany

ISBN: (纸本)9783642247996

Life science research aims at understanding the relationships in genomics, proteomics and metabolomics on all levels of biological self organization, dealing with data of increasing dimension and complexity. bioimages represent a new data domain in this context, gaining growing attention since it closes important gaps left by the established molecular techniques. We present a new, web-based strategy that allows a new way of collaborative bioimage interpretaion through knowledge integration. We show, how this can be supported by combining data mining algorithms running on powerful compute servers and a next generation rich internet application (RIA) front-end offering database/project management and high-level tools for exploratory data analysis and annotation. We demonstrate our system BioIMAX using a bioimage dataset from High-Content Screening experiments to study bacterial infection in cell cultures.

关键词： Life Science bioimage informatics Data Mining Exploratory Data Analysis Information Visualization High-content screening Web2.0 Rich Internet Application Semantic Annotation

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ANALYZING MULTI-TAG bioimageS WITH BIOIMAX COLOCATION MINING TOOLS

ANALYZING MULTI-TAG BIOIMAGES WITH BIOIMAX COLOCATION MINING...

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9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Koelling, Jan Rathke, Magnus Gardner, Dominic Nattkemper, Tim W. Abouna, Sylvie Khan, Michael Univ Bielefeld Fac Technol Biodata Min Grp Bielefeld Germany Biomed Res Inst Sch Life Sci Coventry W Midlands England

ISBN: (纸本)9781457718588

The application of multi-tag protocols in fluorescence microscopy allows the visualization of a large number (> 10) of molecules (i.e. proteins) in a sample (like a tissue section). However, the analysis of such high dimensional bioimages is a difficult task for most of the labs, since software solutions for particular data mining steps are difficult to use or just not available. In this paper we present two new free online tools: MICOLT (Multivariate Image COlocation Tool) and MIFIST (Multivariate Image Frequent Item Set Tool). Both tools can be used via our recently proposed online bioimage analysis platform BioIMAX, so users can upload their bioimage data, apply the tools and share the results with other invited users based on BioIMAX' concept of shared virtual projects. Data mining with these tools includes the computation and visualization colocation factors well established in the microscopy community (like Mander's score) and association rule mining following the frequent item set principle, thereby supporting large and small scale analysis.

关键词： bioimage informatics fluorescence microscopy proteomics tissue analysis data mining visualization web systems science 2.0

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MIC_Locator: a novel image-based protein subcellular location multi-label prediction model based on multi-scale monogenic signal representation and intensity encoding strategy

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BMC BIOinformatics 2019年第1期20卷 522-522页

作者： Yang, Fan Liu, Yang Wang, Yanbin Yin, Zhijian Yang, Zhen Jiangxi Sci & Technol Normal Univ Sch Commun & Elect Nanchang 330003 Jiangxi Peoples R China Harvard Med Sch Dept Biol Chem & Mol Pharmacol Boston MA 02115 USA

Background Protein subcellular localization plays a crucial role in understanding cell function. Proteins need to be in the right place at the right time, and combine with the corresponding molecules to fulfill their functions. Furthermore, prediction of protein subcellular location not only should be a guiding role in drug design and development due to potential molecular targets but also be an essential role in genome annotation. Taking the current status of image-based protein subcellular localization as an example, there are three common drawbacks, i.e., obsolete datasets without updating label information, stereotypical feature descriptor on spatial domain or grey level, and single-function prediction algorithm's limited capacity of handling single-label database. Results In this paper, a novel human protein subcellular localization prediction model MIC_Locator is proposed. Firstly, the latest datasets are collected and collated as our benchmark dataset instead of obsolete data while training prediction model. Secondly, Fourier transformation, Riesz transformation, Log-Gabor filter and intensity coding strategy are employed to obtain frequency feature based on three components of monogenic signal with different frequency scales. Thirdly, a chained prediction model is proposed to handle multi-label instead of single-label datasets. The experiment results showed that the MIC_Locator can achieve 60.56% subset accuracy and outperform the existing majority of prediction models, and the frequency feature and intensity coding strategy can be conducive to improving the classification accuracy. Conclusions Our results demonstrate that the frequency feature is more beneficial for improving the performance of model compared to features extracted from spatial domain, and the MIC_Locator proposed in this paper can speed up validation of protein annotation, knowledge of protein function and proteomics research.

关键词： bioimage informatics Protein subcellular localization Frequency domain feature Monogenic signal Image intensity encoding strategy Multi-label classifier chain

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Evaluation of the effectiveness of simple nuclei-segmentation methods on Caenorhabditis elegans embryogenesis images

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BMC BIOinformatics 2013年第1期14卷 1-11页

作者： Azuma, Yusuke Onami, Shuichi RIKEN Quantitat Biol Ctr Lab Dev Dynam Chuo Ku Kobe Hyogo 6500047 Japan Japan Sci & Technol Agcy Natl Biosci Database Ctr Chiyoda Ku Tokyo 1020081 Japan

Background: For the analysis of spatio-temporal dynamics, various automated processing methods have been developed for nuclei segmentation. These methods tend to be complex for segmentation of images with crowded nuclei, preventing the simple reapplication of the methods to other problems. Thus, it is useful to evaluate the ability of simple methods to segment images with various degrees of crowded nuclei. Results: Here, we selected six simple methods from various watershed based and local maxima detection based methods that are frequently used for nuclei segmentation, and evaluated their segmentation accuracy for each developmental stage of the Caenorhabditis elegans. We included a 4D noise filter, in addition to 2D and 3D noise filters, as a pre-processing step to evaluate the potential of simple methods as widely as possible. By applying the methods to image data between the 50- to 500-cell developmental stages at 50-cell intervals, the error rate for nuclei detection could be reduced to <= 2.1% at every stage until the 350-cell stage. The fractions of total errors throughout the stages could be reduced to <= 2.4%. The error rates improved at most of the stages and the total errors improved when a 4D noise filter was used. The methods with the least errors were two watershed-based methods with 4D noise filters. For all the other methods, the error rate and the fraction of errors could be reduced to <= 4.2% and <= 4.1%, respectively. The minimum error rate for each stage between the 400- to 500-cell stages ranged from 6.0% to 8.4%. However, similarities between the computational and manual segmentations measured by volume overlap and Hausdorff distance were not good. The methods were also applied to Drosophila and zebrafish embryos and found to be effective. Conclusions: The simple segmentation methods were found to be useful for detecting nuclei until the 350-cell stage, but not very useful after the 400 cell stage. The incorporation of a 4D noise filter to the s

关键词： bioimage informatics Nuclei segmentation Image processing Crowded nuclei Caenorhabditis elegans Embryonic development

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Rapid discrimination of fungal species by the colony fingerprinting

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BIOSENSORS & BIOELECTRONICS 2019年 146卷 111747-000页

作者： Maeda, Yoshiaki Sugiyama, Yui Lim, Tae-Kyu Harada, Manabu Yoshino, Tomoko Matsunaga, Tadashi Tanaka, Tsuyoshi Tokyo Univ Agr & Technol Inst Engn Div Biotechnol & Life Sci 2-24-16 Naka Cho Koganei Tokyo 1848588 Japan Malcom Co Ltd Shibuya Ku 4-15-10 Honmachi Tokyo 1510071 Japan Waseda Univ Waseda Res Inst Sci & Engn Shinjuku Ku Tokyo 1698555 Japan

The contamination of foods and beverages by fungi is a severe health hazard. The rapid identification of fungi species in contaminated goods is important to avoid further contamination. To this end, we developed a fungal discrimination method based on the bioimage informatics approach of colony fingerprinting. This method involves imaging and visualizing microbial colonies (referred to as colony fingerprints) using a lens-less imaging system. Subsequently, the quantitative image features were extracted as discriminative parameters and subjected to analysis using machine learning approaches. Colony fingerprinting has been previously found to be a promising approach to discriminate bacteria. In the present proof-of-concept study, we tested whether this method is also useful for fungal discrimination. As a result, 5 fungi belonging to the Aspergillus, Peniciliwn, Eurottum, Alternaria, and Fusarium genera were successfully discriminated based on the extracted parameters, including the number of hyphae and their branches, and their intensity distributions on the images. The discrimination of 6 closely-related Aspergillus spp. was also demonstrated using additional parameters. The cultivation time required to generate the fungal colonies with a sufficient size for colony fingerprinting was less than 48 h, shorter than those for other discrimination methods, including MALDI-TOF-MS. In addition, colony fingerprinting did not require any cumbersome pre-treatment steps prior to discrimination. Colony fingerprinting is promising for the rapid and easy discrimination of fungi for use in the ensuring the safety of food manufacturing.

关键词： Colony fingerprinting bioimage informatics Fungi Machine learning Hyphae Lens-less imaging

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A Novel Methodology for Characterizing Cell Subpopulations in Automated Time-lapse Microscopy

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2018年 6卷 17-17页

作者： Hattab, Georges Wiesmann, Veit Becker, Anke Munzner, Tamara Nattkemper, Tim W. Bielefeld Univ Computat Methods Anal Divers & Dynam Genomes DiDy Int Res Training Grp 1906 Fac Technol Bielefeld Germany Bielefeld Univ Fac Technol Biodata Min Grp Bielefeld Germany Fraunhofer Inst Integrierte Schaltungen IIS Dept Image Proc & Med Engn Erlangen Germany Phillips Univ Marburg LOEWE Ctr Synthet Microbiol SYNMIKRO Marburg Germany Univ British Columbia Dept Comp Sci Vancouver BC Canada

Time-lapse imaging of cell colonies in microfluidic chambers provides time series of bioimages, i.e., biomovies. They show the behavior of cells over time under controlled conditions. One of the main remaining bottlenecks in this area of research is the analysis of experimental data and the extraction of cell growth characteristics, such as lineage information. The extraction of the cell line by human observers is time-consuming and error-prone. Previously proposed methods often fail because of their reliance on the accurate detection of a single cell, which is not possible for high density, high diversity of cell shapes and numbers, and high-resolution images with high noise. Our task is to characterize subpopulations in biomovies. In order to shift the analysis of the data from individual cell level to cellular groups with similar fluorescence or even subpopulations, we propose to represent the cells by two new abstractions: the particle and the patch. We use a three-step framework: preprocessing, particle tracking, and construction of the patch lineage. First, preprocessing improves the signal-to-noise ratio and spatially aligns the biomovie frames. Second, cell sampling is performed by assuming particles, which represent a part of a cell, cell or group of contiguous cells in space. Particle analysis includes the following: particle tracking, trajectory linking, filtering, and color information, respectively. Particle tracking consists of following the spatiotemporal position of a particle and gives rise to coherent particle trajectories over time. Typical tracking problems may occur (e.g., appearance or disappearance of cells, spurious artifacts). They are effectively processed using trajectory linking and filtering. Third, the construction of the patch lineage consists in joining particle trajectories that share common attributes (i.e., proximity and fluorescence intensity) and feature common ancestry. This step is based on patch finding, patching trajectory pr

关键词： bioimaging bioimage informatics cell lineage bacteria microfluidics synthetic biology image processing

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