为了克服传统肝脏肿瘤分割网络下采样带来的细节信息丢失问题,同时提取丰富的多尺度信息,提出了一种基于堆叠树形聚合结构空洞卷积的肝脏肿瘤分割算法。首先,在编码器网络中提出了残差密集模块;然后,在编码器-解码器网络中加入树形聚合结构的空洞卷积模块,有效消除了普通空洞卷积带来的棋盘伪影现象,提高了分割精度。最后,用加权的损失函数解决了图像中前景和背景不平衡的问题。实验结果表明,本算法在电子计算机断层扫描图像数据集上的Dice相似度系数、像素正确率和交并比分别为0.8026、0.7974和0.7317。

In order to overcome the loss of detail information caused by down sampling of traditional liver tumor segmentation networks and extract rich multi-scale information at the same time, this paper proposes an algorithm of liver tumor segmentation based on dilated convolution of stacked tree aggregation structure. First, a residual dense module is proposed in the encoder network. Then, a dilated convolution module of stacked tree aggregation structure is added to the encoder-decoder network, which can effectively eliminate the checkerboard artifacts caused by ordinary dilated convolution and improve the segmentation accuracy. Finally, a weighted loss function is used to solve the problem of the imbalance between the foreground and the background in the image. The experimental results show that the Dice similarity coefficient, pixel accuracy rate and intersection ratio of the algorithm on the computer tomography image data set are 0.8026, 0.7974 and 0.7317, respectively.