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.
| Author of HS Reutlingen | 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: | Journal article |
| Language: | English |
| Publication year: | 2018 |
| Tag: | convolutional neural network; lamellipodia |
| Volume: | 4 |
| Issue: | 1 |
| Page Number: | 4 |
| First Page: | 449 |
| Last Page: | 452 |
| DDC classes: | 570 Biowissenschaften, Biologie |
| Open access?: | Ja |
| Licence (German): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
