论文标题
用于单元图片分割的反馈U-NET
Feedback U-net for Cell Image Segmentation
论文作者
论文摘要
人脑是一种分层的结构，不仅执行从下层到上层的进料过程，而且还执行从上层到下层的反馈过程。该层是神经元的集合，神经网络是神经元功能的数学模型。尽管神经网络模仿了人的大脑，但每个人都仅使用从下层到上层的进料过程，并且不使用从上层到下层的反馈过程。因此，在本文中，我们使用卷积LSTM提出了反馈U-NET，这是使用卷积LSTM和反馈过程的分割方法。 U-NET的输出给出了输入的反馈，并执行第二轮。通过使用卷积LSTM，根据第一轮获得的功能提取第二轮的特征。在果蝇细胞图像和鼠标细胞图像数据集上，我们的方法优于仅使用前馈过程的常规U-NET。
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.
