# CDA-Net for Automatic Prostate Segmentation in MR Images
## 3 Methods
### RAU-Net for Prostate Segmentation  
#### 3.2.1 The Architecture of RAU-Net  
Recently, U-Net has become the backbone for many medical image segmentation tasks due to its ingenious U-shaped architecture and its low data demand [9; 18; 19; 23; 24; 44]. The network consists of encoding and decoding paths. The encoder performs feature extraction and captures context information through a series of convolution and downsampling operations. The decoder is gradually restored to the original image size through upsampling to produce segmentation maps. Besides, the short connection between the codec parts enables the feature maps of the decoder to incorporate more low-level features, thereby increasing the segmentation accuracy of the model.  
At present, the variant ideas of the network are mainly concentrated in two aspects. The first is how to better use the image features of different levels in the encoding and decoding stage, such as introducing the idea of dense connections or residual connections. The second is how to establish a better connection between the encoding and decoding stages to achieve better information fusion.  
Figure 6: Sample images with (**a**) multiple object bounding boxes and (**b**) without object bounding box (red: the true contour of the prostate, green: bounding boxes detected by the network that exceed a threshold of 0.80).  
Figure 7: Schematic diagram of spatial curve fitting and adjustment (x-direction is the sequence direction, \(y\)-axis and \(z\)-axis represent the width and height of the corresponding slice image, respectively).  
This paper draws on the above two ideas. We introduce the residual mechanism and soft attention mechanism to build an encoder-decoder architecture to achieve fine segmentation of the prostate organs. The architecture diagram of the network is shown in Figure 8.  
The network inputs a 2D image to be segmented and outputs a probability prediction map of the same size as the input image. In order to better improve the network's utilization of features at different levels and ensure the network's training convergence during the network deepening process, this paper introduces ResConv Block before downsampling and upsampling operations in the encoding and decoding stages. It consists of two Conv/BN blocks (as shown in Figure 9a). The BatchNormalization layer improves the gradient dispersion by normalizing the convolution output and speeds up the network convergence [45]. The encoding stage is composed of seven sub-blocks, which comprise ResConv Block and Max pooling. Each time the input feature map passes through the ResConv Block, the number of channels is doubled (i.e., \(N_{l+1}=N_{l}\times 2\)), and the image size halved after each Max pooling (i.e., \(H_{l+1}=H_{l}/2,W_{l+1}=W_{l}/2\)). As the number of downsampling increases, the receptive field of feature extraction gradually increases. Therefore, the network's grasp of image details and features gradually weakens, and the grasp of image deep semantic information gradually increases. Symmetrically, the decoding stage consists of seven sub-blocks, which are composed of ResConv Block and upsampling. Each time the input passes through the ResConv Block, the number of channels is halved, and the image size is doubled after each pass through upsampling.  
Figure 8: The architecture of RAU-Net.