Cell image segmentation by using feedback and convolutional LSTM

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Abstract
Human brain is known to have a layered structure and perform not only feedforward process from lower layer to upper layer, but also feedback process from upper layer to lower layer. Neural network is a mathematical model of the function of neurons, and several models are proposed until now. Although neural network imitates the human brain, everyone uses only feedforward process and direct feedback process from upper layer to lower layer is not used in prediction process. Therefore, in this paper, we propose Feedback U-Net using convolutional LSTM. Our model is a segmentation model using convolutional LSTM and feedback process. The output of U-Net at the first round is fed back to the input, and our method re-considers the segmentation result at the second round. By using convolutional LSTM, the features are extracted well based on the features extracted at the first round. On both of the Drosophila cell image and Mouse cell image datasets, our model outperformed conventional U-Net which uses only feedforward process.
Funding Information
  • MEXT/JSPS KAKENHI (18H04746, 18K11382)