  Studying cell morphology changes in time is critical to understanding cell
migration mechanisms. In this work, we present a deep learning-based workflow
to segment cancer cells embedded in 3D collagen matrices and imaged with
phase-contrast microscopy. Our approach uses transfer learning and recurrent
convolutional long-short term memory units to exploit the temporal information
from the past and provide a consistent segmentation result. Lastly, we propose
a geometrical-characterization approach to studying cancer cell morphology. Our
approach provides stable results in time, and it is robust to the different
weight initialization or training data sampling. We introduce a new annotated
dataset for 2D cell segmentation and tracking, and an open-source
implementation to replicate the experiments or adapt them to new image
processing problems.
