Open AccessDetection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion
Author(s) -
Nikolaus Korfhage,
Markus Mühling,
Stephan Ringshandl,
Anke Becker,
Bernd Schmeck,
Bernd Freisleben
Publication year - 2020
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1008179
Subject(s) - segmentation , artificial intelligence , pyramid (geometry) , pattern recognition (psychology) , feature (linguistics) , nucleus , fusion , computer science , image segmentation , computer vision , microscopy , feature extraction , biology , microbiology and biotechnology , physics , optics , linguistics , philosophy
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. Our experimental results indicate that cell detection and segmentation performance significantly benefit from the fusion of previously learned nucleus features. The proposed feature pyramid fusion architecture clearly outperforms a state-of-the-art Mask R-CNN approach for cell detection and segmentation with relative mean average precision improvements of up to 23.88% and 23.17%, respectively.