# CDA-Net for Automatic Prostate Segmentation in MR Images
## 4 Experiments and Discussion
### Experiment Setting and Evaluations
#### 4.2.2 Ablation Experiments
Since slice images with high imaging quality and clear organ contours have almost achieved perfect segmentation performance in many segmentation algorithms, especially those based on deep learning, this paper does not involve much of such images. As shown in Figure 10,  
* The first column compares the segmentation performance of each algorithm in the images with endorectal coils. It can be seen that the four algorithms based on the U-Net architecture are not excessively affected by coil artifacts and image contrast, and they have achieved relatively good performance;
* The second column shows the performance of the four algorithms when the gray distribution inside the gland is obviously uneven. We can see that the predicted segmentation results of the U-Net-1 and ResU-Net algorithms are affected to varying degrees at this time. In contrast, the contours obtained by the RAU-Net and CDA-Net proposed in this paper can still have a good matching effect with the real segmentation contour, which fully reveals that the attention gate mechanism added in this paper can have a more accurate estimation of attention to the region where the organ is located, thereby guiding the algorithm to obtain a more reasonable segmentation result;
* Besides, columns 3-5 specifically compare the segmentation performance of the algorithm on the apex and base of the slice sequences. It can be observed that the proportion of organs in the slice images at both ends of the organ is very small, and the contours of the organs in the base slices \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & & **DSC** & **RVD** & **SA** & **UR** & **OR** \\ \hline \multirow{2}{*}{U-Net-1} & Avg & 0.8893 & 0.0438 & 0.8697 & 0.0047 & 0.0029 \\  & Std & 4.6 & 8.0 & 5.2 & 4.7 & 4.5 \\ \hline \multirow{2}{*}{ResU-Net} & Avg & 0.8935 & 0.0629 & 0.8700 & 0.0045 & 0.0025 \\  & Std & 3.9 & 7.6 & 4.8 & 4.0 & 3.8 \\ \hline \multirow{2}{*}{RAU-Net} & Avg & 0.9017 & 0.0190 & 0.8931 & 0.0037 & 0.0030 \\  & Std & 3.5 & 7.3 & 4.7 & 3.7 & 3.6 \\ \hline \multirow{2}{*}{CDA-Net} & Avg & 0.9288 & 0.0248 & 0.9403 & 0.0033 & 0.0036 \\  & Std & 3.4 & 6.7 & 4.4 & 3.6 & 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of four algorithms on PROMISE12. are often blurred. Therefore, this is usually the most challenging part of prostate segmentation, and it is also the key part that can distinguish the performance of the algorithm. Comparing the segmentation results of the four algorithms in the third and fourth columns, we can see that the algorithms ResU-Net and RAU-Net can better obtain the position of the organs than U-Net-1, but the accurate segmentation of the organs is still inferior to CDA-Net;
* Similarly, in the fifth column, even though the organs are relatively small and the contours are irregular, the algorithm CDA-Net in this paper still achieves a better segmentation performance than the first three algorithms. It demonstrates that the initial ROI extraction based on the sequence correlation used in this paper can effectively locate the organ position, thereby greatly reducing the focus of subsequent fine segmentation, and making the CDA-Net algorithm present more significant advantages in the segmentation of prostate base and apex. **Experiment 2. ** Ablation Experiments on ASPS13  
Table 4 lists the average results of the above four algorithms tested on the ASPS13 dataset. It can be seen that because the ASPS13 dataset is relatively less complex than the PROMISE12 dataset, it shows a more excellent segmentation performance in the ResU-Net and RAU-Net algorithms without ROI extraction. In addition, the segmentation results gap between RAU-Net and ResU-Net and between ResU-Net and U-Net-1 is obviously widened. This shows that in the case of relatively low image complexity, the residual convolution block and soft-attention mechanism introduced in RAU-Net can better mine the generalized features of the image, thereby better improving the pixel prediction effect.