Automatic assessment of the cardiomyocyte development stages from confocal microscopy images using deep convolutional networks

PLOS 2019-05-30

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.

cardiomyocyte development stages; densely connected convolutional network; deep learning; classification of object images; confocal microscopy

Open access content. Open access content
http://creativecommons.org/licenses/by/4.0
openAccess
http://www.sherpa.ac.uk/romeo/issn/1932-6203
Creative Commons Attribution 4.0 International

5
14
English

CZBUT oai:dspace.vutbr.cz:11012/179583
PLOS ONE. 2019, vol. 14, issue 5, p. 1-18.
1932-6203
157176
10.1371/journal.pone.0216720
1121204118

BRNO UNIV OF TECHNOL
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