Automatic assessment of the cardiomyocyte development stages from confocal microscopy images using deep convolutional networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F19%3APU132244" target="_blank" >RIV/00216305:26210/19:PU132244 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1371/journal.pone.0216720" target="_blank" >https://doi.org/10.1371/journal.pone.0216720</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1371/journal.pone.0216720" target="_blank" >10.1371/journal.pone.0216720</a>
Alternative languages
Result language
angličtina
Original language name
Automatic assessment of the cardiomyocyte development stages from confocal microscopy images using deep convolutional networks
Original language description
Computer assisted image acquisition techniques, including confocal microscopy, require efficient tools for an automatic sorting of vast amount of generated image data. The complexity of the classification process, absence of adequate tools, and insufficient amount of reference data has made the automated processing of images challenging. Mastering of this issue would allow implementation of statistical analysis in research areas such as in research on formation of t-tubules in cardiac myocytes. We developed a system aimed at automatic assessment of cardiomyocyte development stages (SAACS). The system classifies confocal images of cardiomyocytes with fluorescent dye stained sarcolemma. We based SAACS on a densely connected convolutional network (DenseNet) topology. We created a set of labelled source images, proposed an appropriate data augmentation technique and designed a class probability graph. We showed that the DenseNet topology, in combination with the augmentation technique is suitable for the given task, and that high-resolution images are instrumental for image categorization. SAACS, in combination with the automatic high-throughput confocal imaging, will allow application of statistical analysis in the research of the tubular system development or remodelling and loss.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PLOS ONE
ISSN
1932-6203
e-ISSN
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Volume of the periodical
14
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
1-18
UT code for WoS article
000469425500004
EID of the result in the Scopus database
2-s2.0-85066448727