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.

众所周知，人脑具有分层结构，不仅执行从下层到上层的前馈过程，还执行从上层到下层的反馈过程。神经网络是神经元功能的数学模型，迄今为止提出了几种模型。神经网络虽然模仿人脑，但大家都只使用前馈过程，预测过程不使用从上层到下层的直接反馈过程。因此，在本文中，我们提出了使用卷积 LSTM 的反馈 U-Net。我们的模型是使用卷积 LSTM 和反馈过程的分割模型。第一轮 U-Net 的输出反馈给输入，我们的方法在第二轮重新考虑分割结果。通过使用卷积 LSTM，根据第一轮提取的特征很好地提取了特征。在果蝇细胞图像和小鼠细胞图像数据集上，我们的模型都优于仅使用前馈过程的传统 U-Net。