Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion

PLoS Comput Biol. 2020 Sep 8;16(9):e1008179. doi: 10.1371/journal.pcbi.1008179. eCollection 2020 Sep.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology
  • Deep Learning
  • Eukaryotic Cells / cytology*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Macrophages / cytology
  • Microscopy, Fluorescence / methods*
  • Neural Networks, Computer*
  • THP-1 Cells

Grants and funding

This work has been supported by the HMWK (LOEWE SYNMIKRO Research Center and its Research Core Facility ‘Screening and Automation Technologies’, LOEWE MOSLA Research Cluster, and LOEWE Medical RNomics Research Cluster), DFG (SFB/TR-84 TP C01), and BMBF (e:Med CAPSYS, JPI-AMR, ERACoSysMed2 - SysMed-COPD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.