Abstract
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.
Highlights
Bacterial cell shapes can vary from cocci and rods to more exotic shapes such as spirals or branches (Kysela et al, 2016)
The results show that automatic identification of longitudinal division is possible using Residual Network (ResNet)
We do not have models previously trained in our classification problem; we were able to exploit the advantages of a general model like ResNet to successfully classify the longitudinal division of bacteria
Summary
Bacterial cell shapes can vary from cocci and rods to more exotic shapes such as spirals or branches (Kysela et al, 2016). Microscopy approaches are commonly used to observe and classify different microorganisms according to their different morphological features. Microscopic cell counting is one of the most common techniques used to measure microbial growth. This approach usually relies on automatic microscopic cell counting using digital image analysis software in order to determine division rates (Daims et al, 2006; Nekrasov et al, 2013). Longitudinal bacterial division (or fission) is a rare feature among bacteria (Pende et al, 2018). Discriminating between perpendicular and longitudinal division requires novel approaches in image analysis to differentiate those cells undergoing a division, whereby they widen instead of elongating
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