Speaker
Description
Cellular analysis based on microscopy images starts with the identification of cells, typically by segmentation. This challenges researchers to construct out-of-the-box solutions that potentially work in various experiments as downstream analysis depends on segmentation reliability. nucleAIzer is a deep learning-based pipeline intended for the efficient and robust instance segmentation of cellular compartments, even on such new image modalities for which no ground truth data is available by adaptation to them via image style transfer learning. With this technique we can generate synthetic images in the new, unknown experiments' domain and forward this information to the training of a segmentation model, thus preparing it to cope with such images.
Title
nucleAIzer: nucleus segmentation with DL & image style transfer
| authors | Reka Hollandi1, Abel Szkalisity1, Timea Toth1,2, Ervin Tasnadi1,3, Csaba Molnar1,3, Botond Mathe1, Istvan Grexa1,4, …, Ferenc Kovacs1,8, Lassi Paavolainen7, Tivadar Danka1, Andras Kriston1,8, Anne Elizabeth Carpenter6, Kevin Smith9,10, Peter Horvath1,7 |
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| affiliation | 1. Biological Research Center, Szeged, Hungary. 2,3,4. University of Szeged, Szeged, Hungary. 6. Broad Institute of Harvard and MIT, USA. 7. FIMM, Helsinki, Finland. 8. Single-Cell Technologies Ltd, Hungary… |
