  Human brain is a layered structure, and performs not only a feedforward
process from a lower layer to an upper layer but also a feedback process from
an upper layer to a lower layer. The layer is a collection of neurons, and
neural network is a mathematical model of the function of neurons. Although
neural network imitates the human brain, everyone uses only feedforward process
from the lower layer to the upper layer, and feedback process from the upper
layer to the lower layer is not used. Therefore, in this paper, we propose
Feedback U-Net using Convolutional LSTM which is the segmentation method using
Convolutional LSTM and feedback process. The output of U-net gave feedback to
the input, and the second round is performed. By using Convolutional LSTM, the
features in the second round are extracted based on the features acquired in
the first round. On both of the Drosophila cell image and Mouse cell image
datasets, our method outperformed conventional U-Net which uses only
feedforward process.
