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Feature Pyramid Fusion for Detection and Segmentation of Morphologically Complex Eukaryotic Cells

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Models and Datasets for the following publication: Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion Korfhage N, Mühling M, Ringshandl S, Becker A, Schmeck B, et al. (2020) Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion. PLOS Computational Biology 16(9): e1008179. https://doi.org/10.1371/journal.pcbi.1008179
Detection and segmentation of macrophage cells in fluorescence microscopy images is a challenging problem, mainly due to crowded cells, variation in shapes, and morphological complexity. We present a new deep learning approach for cell detection and segmentation that incorporates previously learned nucleus features. A novel fusion of feature pyramids for nucleus detection and segmentation with feature pyramids for cell detection and segmentation is used to improve performance on a microscopic image dataset created by us and provided for public use, containing both nucleus and cell signals.
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Korfhage, Nikolaus; Ringshandl, Stephan; Becker, Anke; Schmeck, Bernd; Mühling, Markus; Freisleben, Bernd: Feature Pyramid Fusion for Detection and Segmentation of Morphologically Complex Eukaryotic Cells. .