TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Grützmacher, Simon A1 - Kemkemer, Ralf A1 - Thies, Christian A1 - Curio, Cristóbal T1 - Detecting lamellipodia in epithelial cell clusters using a fully convolutional neural network for phase contrast microscopy images JF - Current directions in biomedical engineering N2 - 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. KW - lamellipodia KW - convolutional neural network Y1 - 2018 UN - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-21877 SN - 2364-5504 SS - 2364-5504 U6 - https://doi.org/10.1515/cdbme-2018-0107 DO - https://doi.org/10.1515/cdbme-2018-0107 VL - 4 IS - 1 SP - 449 EP - 452 S1 - 4 PB - De Gruyter CY - Berlin ER -