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Classification of first embryonic division stages of multiple Caenorhabditis species by deep learning

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Using deep learning, CNNs accurately classify cell division stages in Caenorhabditis, highlighting asymmetric division mechanisms.

The classification pipeline achieved over 91% accuracy in identifying distinct embryonic division stages.
Activation vectors from the networks correlate with features like pro-nuclei and spindle structures across multiple species.
Deep convolutional neural networks were trained to classify cell stages using DIC microscopy of various nematode embryos.
Classification success is contingent on conserved morphological features across comparable species in DIC imagery.

Khatri et al. (2025) studied this question.

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View Full Paper

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Authors

Using deep learning, CNNs accurately classify cell division stages in Caenorhabditis, highlighting asymmetric division mechanisms.

The classification pipeline achieved over 91% accuracy in identifying distinct embryonic division stages.
Activation vectors from the networks correlate with features like pro-nuclei and spindle structures across multiple species.
Deep convolutional neural networks were trained to classify cell stages using DIC microscopy of various nematode embryos.
Classification success is contingent on conserved morphological features across comparable species in DIC imagery.

Khatri et al. (2025) studied this question.

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