Zusammenfassung
Segmentation and quantification of cell nuclei is an important task in tissue microscopy image analysis. We introduce a deep learning method leveraging atrous spatial pyramid pooling for cell segmentation. We also present two different approaches for transfer learning using datasets with a different number of channels. A quantitative comparison with previous methods was performed on challenging glioblastoma cell tissue images. We found that our transfer learning method improves the segmentation result.
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Wollmann, T. et al. (2018). Multi-channel Deep Transfer Learning for Nuclei Segmentation in Glioblastoma Cell Tissue Images. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_83
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DOI: https://doi.org/10.1007/978-3-662-56537-7_83
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