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.

• MeSH
• Cell Differentiation MeSH
• Fluorescent Dyes MeSH
• Myocytes, Cardiac cytology   ultrastructure   MeSH
• Microscopy, Confocal methods   MeSH
• Rats MeSH
• Models, Cardiovascular MeSH
• Neural Networks, Computer MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Sarcolemma ultrastructure   MeSH
• Machine Learning MeSH
• Artificial Intelligence MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH