基于多层级特征自适应融合的图像分割算法

为了解决传统算法对医学图像分割时精度较低的问题，提出基于多层级特征自适应融合的新型FR-Unet图像分割算法. 在编码器阶段，设计采样加权模块替代传统卷积层，对图像空间信息进行逐层提取和特征融合，获得相邻像素之间的相关性和不同层次的语义信息. 在解码器阶段，设计多层级自适应融合模块，通过非线性跳跃连接逐层提取图像通道信息，自适应地融合邻近连接层的上下文信息，使各层专注不同特征信息的提取. FR-Unet在模型参数量上大幅度减少，让网络在场景部署上得到更好的支持. 实验结果表明，该网络在动物细胞分割、肝脏器官分割、皮肤病变分割等众多任务中均表现突出.

Image segmentation algorithm based on multi-level feature adaptive fusion

A novel FR-Unet image segmentation algorithm based on multi-layer feature adaptive fusion was proposed in order to solve the problem of low accuracy when segmenting medical images by traditional algorithms. The sampling weighting module was designed to replace the traditional convolutional layer in the encoder stage. The image spatial information was extracted and fused by layer, and the correlation between neighboring pixels and the semantic information at different levels was obtained. A multi-layer adaptive fusion module was designed in the decoder stage in order to extract image channel information layer by layer through nonlinear jump connections and adaptively fuse the contextual information of neighboring connected layers. Then each layer focuses on the extraction of different feature information. FR-Unet was substantially reduced in the number of model parameters, allowing the network to be better supported in scene deployment. The experimental results show that the network excels in numerous tasks such as animal cell segmentation, liver organ segmentation and skin lesion segmentation.