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On Generative Modeling of Cell Shape Using 3D GANs

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107522" target="_blank" >RIV/00216224:14330/19:00107522 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-030-30645-8_61" target="_blank" >http://dx.doi.org/10.1007/978-3-030-30645-8_61</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-30645-8_61" target="_blank" >10.1007/978-3-030-30645-8_61</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    On Generative Modeling of Cell Shape Using 3D GANs

  • Original language description

    The ongoing advancement of deep-learning generative models, showing great interest of the scientific community since the introduction of the generative adversarial networks (GAN), paved the way for generation of realistic data. The utilization of deep learning for the generation of realistic biomedical images allows one to alleviate the constraints of the parametric models, limited by the employed mathematical approximations. Building further upon the laid foundation, the 3D GAN added another dimension, allowing generation of fully 3D volumetric data. In this paper, we present an approach to generating fully 3D volumetric cell masks using GANs. Presented model is able to generate high-quality cell masks with variability matching the real data. Required modifications of the proposed model are presented along with the training dataset, based on 385 real cells captured using the fluorescence microscope. Furthermore, the statistical validation is also presented, allowing to quantitatively assess the quality of data generated by the proposed model.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA17-05048S" target="_blank" >GA17-05048S: Multi-modal live cell image segmentation and tracking</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Image Analysis and Processing – ICIAP 2019

  • ISBN

    9783030306441

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    11

  • Pages from-to

    672-682

  • Publisher name

    Springer

  • Place of publication

    Trento

  • Event location

    Trento, Italy

  • Event date

    Jan 1, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000562008400059