Feedback U-net for Cell Image Segmentation
This is an incremental improvement for biomedical image analysis, specifically enhancing segmentation accuracy in cell imaging.
The paper tackles cell image segmentation by proposing Feedback U-Net, which incorporates a feedback process using Convolutional LSTM to improve feature extraction, and it outperforms conventional U-Net on Drosophila and Mouse cell image datasets.
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.