基于多尺度特征和注意力机制的肝脏组织病理图像语义分割网络

针对肝脏组织病理图像分割中存在的正常组织和异常组织过渡区域较难分割和空洞较多的问题,设计基于多尺度特征和注意力机制的肝脏组织病理图像语义分割网络.在编码器中提取融合多尺度特征,改善正常组织和异常组织过渡区域的分割效果.同时利用注意力机制对空间维度和通道维度进行相关性建模,获得每个像素类内响应和通道间的依赖关系,缓解肝脏组织病理图像空洞较多对网络学习带来的影响.实验表明文中网络可较快速准确分割肝脏组织病理图像损伤区域.

Semantic Segmentation Network of Pathological Images of Liver Tissue Based on Multi-scale Feature and Attention Mechanism

To address the problem of difficult segmentation and many voids in the transition regions of normal and abnormal tissues in liver histopathology images segmentation, a semantic segmentation network of pathological images of liver tissue based on multi-scale feature and attention mechanism is proposed. The fused multi-scale features are extracted in the encoder to improve the segmentation of the transition regions between normal and abnormal tissues. The attention mechanism is employed to model the correlation between spatial dimension and channel dimension to obtain the response of each pixel within each class as well as the dependency between channels, and the impact of many voids in liver histopathology images on the network learning is alleviated. Experiments demonstrate that the proposed network can segment the damaged regions of liver histopathology images more quickly and accurately.