基于卷积胶囊编码器和多尺度局部特征共现的图像分割网络

U-Net在图像分割领域取得了巨大成功，然而卷积和下采样操作导致部分位置信息丢失，全局和长距离的语义交互信息难以被学习，并且缺乏整合全局和局部信息的能力。为了提取丰富的局部细节和全局上下文信息，提出了一个基于卷积胶囊编码器和局部共现的医学图像分割网络MLFCNet (network based on convolution capsule encoder and multi-scale local feature co-occurrence)。在U-Net基础上引入胶囊网络模块，学习目标位置信息、局部与全局的关系。同时利用提出的注意力机制保留网络池化层丢弃的信息，并且设计了新的多尺度特征融合方法，从而捕捉全局信息并抑制背景噪声。此外，提出了一种新的多尺度局部特征共现算法，局部特征之间的关系能够被更好地学习。在两个公共数据集上与九种方法进行了比较，相比于性能第二的模型，该方法的mIoU在肝脏医学图像中提升了4.7%,Dice系数提升了1.7%。在肝脏医学图像和人像数据集上的实验结果表明，在相同的实验条件下，提出的网络优于U-Net和其他主流的图像分割网络。

Medical image segmentation network based on convolution capsule encoder and multi-scale local feature co-occurrence

U-Net has achieved great success in the field of image segmentation. However, some of the position information is lost in the process of convolution and downsampling, model is difficult to learn global and long-range semantic interaction information and lacks the ability to integrate global and local information. To extract rich local detail and contextual information, this paper proposed an image segmentation network called MLFCNet, combining a convolutional module and a capsule encoder. Based on the U-Net, this paper introduced a capsule network module to learn target positional information and the relationships between local and global information. At the same time, the proposed attention mechanism could retain the information discarded by the network pooling layer. This paper designed a new attention mechanism so that multi-scale features could be fused, where global information was captured and background noise was suppressed. In addition, it proposed a new local feature co-occurrence algorithm to better learn the relationship between local features. The proposed method was compared with nine methods on two public datasets, mIoU improves 4.7% and Dice coefficient improves 1.7% in liver medical images compared to the second highest performing model. Experimental results on the dataset of liver and dataset of human show that under the same experimental conditions, the proposed network is superior to U-Net and other mainstream image segmentation networks.