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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.


CiteScore 2018: 0.47

Source Normalized Impact per Paper (SNIP) 2018: 0.377

Open Access
Online
ISSN
2364-5504
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Detecting Lamellipodia in Epithelial Cell Clusters Using a Fully Convolutional Neural Network for Phase Contrast Microscopy Images

Simon Grützmacher / Ralf Kemkemer
  • Dept. of Applied Chemistry, Reutlingen University & Max Planck Institute for Medical Research, Heidelberg, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Christian Thies / Cristóbal Curio
Published Online: 2018-09-22 | DOI: https://doi.org/10.1515/cdbme-2018-0107

Abstract

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.

Keywords: lamellipodia; convolutional neural network; supervised learning; segmentation

About the article

Published Online: 2018-09-22

Published in Print: 2018-09-01


Citation Information: Current Directions in Biomedical Engineering, Volume 4, Issue 1, Pages 449–452, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2018-0107.

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