  Live cell microscopy sequences exhibit complex spatial structures and
complicated temporal behaviour, making their analysis a challenging task.
Considering cell segmentation problem, which plays a significant role in the
analysis, the spatial properties of the data can be captured using
Convolutional Neural Networks (CNNs). Recent approaches show promising
segmentation results using convolutional encoder-decoders such as the U-Net.
Nevertheless, these methods are limited by their inability to incorporate
temporal information, that can facilitate segmentation of individual touching
cells or of cells that are partially visible. In order to exploit cell dynamics
we propose a novel segmentation architecture which integrates Convolutional
Long Short Term Memory (C-LSTM) with the U-Net. The network's unique
architecture allows it to capture multi-scale, compact, spatio-temporal
encoding in the C-LSTMs memory units. The method was evaluated on the Cell
Tracking Challenge and achieved state-of-the-art results (1st on Fluo-N2DH-SIM+
and 2nd on DIC-C2DL-HeLa datasets) The code is freely available at:
https://github.com/arbellea/LSTM-UNet.git
