To obtain more semantic information with small samples for medical image segmentation, this paper proposes a simple and efficient dual-rotation network (DR-Net) that strengthens the quality of both local and global feature maps. The key steps of the DR-Net algorithm are as follows (as shown in Fig. 1). First, the number of channels in each layer is divided into four equal portions. Then, different rotation strategies are used to obtain a rotation feature map in multiple directions for each subimage. Then, the multiscale volume product and dilated convolution are used to learn the local and global features of feature maps. Finally, the residual strategy and integration strategy are used to fuse the generated feature maps. Experimental results demonstrate that the DR-Net method can obtain higher segmentation accuracy on both the CHAOS and BraTS data sets compared to the state-of-the-art methods.

为了用小样本获得更多的语义信息进行医学图像分割，本文提出了一种简单高效的双旋转网络（DR-Net），可以增强局部和全局特征图的质量。DR-Net算法的关键步骤如下（如图1所示）。首先，每层的通道数被分成四个相等的部分。然后，使用不同的旋转策略为每个子图像获得多个方向的旋转特征图。然后，使用多尺度体积积和扩张卷积来学习特征图的局部和全局特征。最后，使用残差策略和集成策略来融合生成的特征图。