基于循环密集连接融合更多局部特征的肝脏分割

由于腹部图像中肝脏区域的复杂性和传统分割方法特征提取上的局限性等原因,肝脏分割领域仍存在着很多挑战.针对现有分割网络在肝脏区域的全局信息和局部信息处理上存在的不足,设计了一种融合更多局部特征的循环密集连接网络的分割方法.该方法将循环密集连接模块和局部特征补充模块整合为编码过程的学习单元,使编码单元融合深层次全局信息和更多尺度的局部特征信息.最后,在解码过程后,利用softmax函数输出分割结果.在LiTS数据集上该方法在多个评价指标中表现优异,精确度达到了95.1％.此外,在Data_67数据集上的相关实验也证明了该方法具有很好的泛化性能.实验表明,密集连接融合更多的局部信息,能够使肝脏分割模型的性能更加优异.

Liver segmentation of circular densely connected network based on more local information

The complexity of the liver region in abdominal images and the limitations of traditional segmentation methods in feature extraction make the field of liver segmentation still have many challenges. Aiming at the shortcomings of the existing segmentation networks in the global and local information processing of the liver region, this paper proposed a segmentation method of cyclic densely connected networks that integrated more local features. This method integrated the cyclic dense connection module and the local feature supplement module into the learning unit of the coding process, so that the coding unit integrated deep-level global information and local feature information on a larger scale. Finally, after the decoding process, this method used the softmax function to output the segmentation result. On LiTS data set, the method performed well in multiple evaluation indicators, with an accuracy of 95.1%. In addition, related experiments on Data_67 dataset also proves that the method had good generalization performance. Experiments show that dense connections and more local information can make the performance of the liver segmentation model better.