A deep learning framework for nucleus segmentation using image style transfer

Hollandi, R.; Szkalisity, A.; Toth, T.; Tasnadi, E.; Molnar, C.; Mathe, B.; Grexa, I.; Molnar, J.; Balind, A.; Gorbe, M.; Kovacs, M.; Migh, E.; Goodman, A.; Balassa, T.; Koos, K.; Wang, W.; Bara, N.; Kovacs, F.; Paavolainen, L.; Danka, T.; Kriston, A.; Carpenter, A. E.; Smith, K.; Horvath, P.

doi:10.1101/580605

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bioRxiv · 10.1101/580605

A deep learning framework for nucleus segmentation using image style transfer

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Abstract

Single cell segmentation is typically one of the first and most crucial tasks of image-based cellular analysis. We present a deep learning approach aiming towards a truly general method for localizing nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is to adapt our model to unseen and unlabeled data using image style transfer to generate augmented training samples. This allows the model to recognize nuclei in new and different experiments without requiring expert annotations.

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Hollandi, R., Szkalisity, A., Toth, T., Tasnadi, E., Molnar, C., Mathe, B., Grexa, I., Molnar, J., Balind, A., Gorbe, M., Kovacs, M., Migh, E., Goodman, A., Balassa, T., Koos, K., Wang, W., Bara, N., Kovacs, F., Paavolainen, L., Danka, T., Kriston, A., Carpenter, A. E., Smith, K., Horvath, P.. 2019-03-17. A deep learning framework for nucleus segmentation using image style transfer. https://doi.org/10.1101/580605

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