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Garcia Perez, C. ; Ito, K. ; Geijo, J.* ; Feldbauer, R.* ; Schreiber, N. ; zu Castell, W.

Efficient detection of longitudinal bacteria fission using transfer learning in deep neural networks.

Front. Microbiol. 12:645972 (2021)

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A very common way to classify bacteria is through microscopic images. Microscopic cell counting is a widely used technique to measure microbial growth. To date, fully automated methodologies are available for accurate and fast measurements; yet for bacteria dividing longitudinally, as in the case of Candidatus Thiosymbion oneisti, its cell count mainly remains manual. The identification of this type of cell division is important because it helps to detect undergoing cellular division from those which are not dividing once the sample is fixed. Our solution automates the classification of longitudinal division by using a machine learning method called residual network. Using transfer learning, we train a binary classification model in fewer epochs compared to the model trained without it. This potentially eliminates most of the manual labor of classifying the type of bacteria cell division. The approach is useful in automatically labeling a certain bacteria division after detecting and segmenting (extracting) individual bacteria images from microscopic images of colonies.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Bacteria Classification ; Bacteria Division ; Deep Learning ; Image Processing ; Image Segmentation ; Longitudinal Bacterial Fission ; Transfer Learning

ISSN (print) / ISBN 1664-302X

e-ISSN 1664-302X

Journal Frontiers in Microbiology

Quellenangaben Volume: 12, Article Number: 645972

Publisher Frontiers

Publishing Place Avenue Du Tribunal Federal 34, Lausanne, Ch-1015, Switzerland

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Strategy and Digitalization (DIG)

Grants Helmholtz Zentrum Munchen Germany