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    Forget About Electron Micrographs: A Novel Guide for Using 3D Models for Quantitative Analysis of Dense Reconstructions

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    Type
    Book Chapter
    Authors
    Boges, Daniya
    Agus, Marco
    Magistretti, Pierre J. cc
    Cali, Corrado
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Bioscience Program
    Date
    2020-06-30
    Online Publication Date
    2020-06-30
    Print Publication Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/664709
    
    Metadata
    Show full item record
    Abstract
    With the rapid evolvement in the automation of serial micrographs, acquiring fast and reliably giga- to terabytes of data is becoming increasingly common. Optical, or physical sectioning, and subsequent imaging of biological tissue at high resolution, offers the chance to postprocess, segment, and reconstruct micro- and nanoscopical structures, and then reveal spatial arrangements previously inaccessible or hardly imaginable with simple, single section, two-dimensional images. In some cases, three-dimensional models highlighted peculiar morphologies in a way that two-dimensional representations cannot be considered representative of that particular object morphology anymore, like mitochondria for instance. Observations like these are taking scientists toward a more common use of 3D models to formulate functional hypothesis, based on morphology. Because such models are so rich in details, we developed tools allowing for performing qualitative, visual assessments, as well as quantification directly in 3D. In this chapter we will revise our working pipeline and show a step-by-step guide to analyze our dataset.
    Citation
    Boges, D. J., Agus, M., Magistretti, P. J., & Calì, C. (2020). Forget About Electron Micrographs: A Novel Guide for Using for Quantitative Analysis of Dense Reconstructions. Neuromethods, 263–304. doi:10.1007/978-1-0716-0691-9_14
    Publisher
    Springer Nature
    ISBN
    9781071606902
    9781071606919
    DOI
    10.1007/978-1-0716-0691-9_14
    Additional Links
    http://link.springer.com/10.1007/978-1-0716-0691-9_14
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-1-0716-0691-9_14
    Scopus Count
    Collections
    Biological and Environmental Science and Engineering (BESE) Division; Bioscience Program; Visual Computing Center (VCC); Book Chapters; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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