Detecting lamellipodia in epithelial cell clusters using a fully convolutional neural network for phase contrast microscopy images

  • We present an approach for segmenting individual cells and lamellipodia in epithelial cell clusters using fully convolutional neural networks. The method will set the basis for measuring cell cluster dynamics and expansion to improve the investigation of collective cell migration phenomena. The fully learning-based front-end avoids classical feature engineering, yet the network architecture needs to be designed carefully. Our network predicts how likely each pixel belongs to one of the classes and, thus, is able to segment the image. Besides characterizing segmentation performance, we discuss how the network will be further employed.

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Metadaten
Name:Grützmacher, Simon; Kemkemer, Ralf; Thies, Christian; Curio, Cristóbal
URN:urn:nbn:de:bsz:rt2-opus4-21877
DOI:https://doi.org/10.1515/cdbme-2018-0107
ISSN:2364-5504
Erschienen in:Current directions in biomedical engineering
Publisher:De Gruyter
Place of publication:Berlin
Document Type:Article
Language:English
Year of Publication:2018
Tag:convolutional neural network; lamellipodia
Volume:4
Issue:1
Pagenumber:4
First Page:449
Last Page:452
Dewey Decimal Classification:570 Biowissenschaften, Biologie
Open Access:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International