To solve the problems of rough edge and poor segmentation accuracy of traditional neural networks in small nucleus image segmentation, a nucleus image segmentation technology based on U-Net network is proposed. First, the U-Net network is used to segment the nucleus image, which stitches the feature images in the channel dimension to achieve feature fusion, and the skip structure is used to combine the low- and high-level features. Then, the subregional average pooling is proposed to improve the global average pooling in the attention module, and an attention channel expansion module is designed to improve the accuracy of image segmentation. Finally, the improved attention module is integrated into the U-Net network to achieve accurate segmentation of the nuclear image. Based on the Python platform, the experimental results show that the proposed segmentation technology can achieve fast convergence, and the mean intersection over union (MIoU) is 85.02%, which is better than other comparison technologies and has a good application prospect.

中文翻译：

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基于U-Net网络的核图像分割技术

针对传统神经网络在小核图像分割中边缘粗糙、分割精度差的问题，提出了一种基于U-Net网络的核图像分割技术。首先使用U-Net网络对核图像进行分割，在通道维度上拼接特征图像以实现特征融合，使用skip结构将低层和高层特征结合起来。然后，在注意力模块中提出了子区域平均池化来改进全局平均池化，并设计了注意力通道扩展模块来提高图像分割的准确性。最后，将改进后的注意力模块集成到 U-Net 网络中，实现核图像的精确分割。基于Python平台，