# CDA-Net for Automatic Prostate Segmentation in MR Images
## 4 Experiments and Discussion
### Experiment Setting and Evaluations
#### 4.2.2 Ablation Experiments  
In this paper, two ablation experiments were conducted on the PROMISE12 test sets and ASPS13 dataset. In order to fully guarantee the effectiveness of the comparison experiment, the U-Net-1 here maintained the same basic architecture parameters as the algorithm RAU-Net in this paper. Based on U-Net [15], the initial network channel number is set to 8, and the downsampling and upsampling depths are both 7. ResU-Net and RAU-Net also use U-Net-1 as the benchmark, and successively add residual convolution blocks and attention gate mechanism. At the same time, the four algorithms are consistent in dataset allocation, image preprocessing, and data augmentation operations. The segmentation effects were assessed by five criteria--DSC, relative volume difference (RVD), segmentation accuracy (SA), oversegmentation rate (OR), undersegmentation rate (UR). * RVD measures the difference between the predicted segmentation volume X and the real segmentation volume Y. \[RVD=\Big{(}\frac{|X|}{|Y|}-1\Big{)}\times 100\%,\] (7)
* SA, which is equivalent to the segmentation recall rate, is calculated as the ratio of correct prediction of pixels in the real foreground area by the algorithm. \[SA=\frac{TP}{TP+FN},\] (8)
* OR represents the oversegmentation rate of the algorithm, which can be described as follows: \[OR=\frac{FP}{TP+FP+FN},\] (9)
* UR reflects the undersegmentation rate of the algorithm, that is \[UR=\frac{FP}{TP+FP+FN},\] (10) where TP, FP, TN, and FN denote the number of true positives, false positives, true negatives, and false negatives. **Experiment 1**. : Ablation Experiments on the test set of PROMISE12  
Table 3 lists the segmentation comparison results of the above four algorithms in the PROMISE12 test set. It can be seen that, \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Mean\_DSC (\%)** \\ \hline FCN & 78.66 \\ U-Net & 86.35 \\ UNet\_S.2.0.1.1 [50] & 89.00 \\ Deep dense multi-path neural network [51] & 89.01 \\ Atlas registration and ensemble deep convolutional neural network [25] & 91.00 \\ HD-net [52] & 91.35 \\ nnU-Net [53] & 91.61 \\ BOWDA-Net [54] & 92.54 \\ CDA-Net (Proposed) & 92.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results with state-of-the-art algorithms. * Compared with U-Net [12], U-Net-1 and ResU-Net have a significant improvement in the value of DSC, which shows that the network's mining of image deep features and full use of features at different levels does improve the effect of the predicted segmentation;
* Besides, the addition of the attention gate mechanism in RAU-Net forces the network to pay more attention to real organ regions, therefore it shows more obvious advantages than the previous two algorithms in the results of multiple metrics, especially RVD and SA;
* Finally, although the CDA-Net proposed in this paper is slightly worse in the OR and RVD, it increases by 2.71% and 4.72% in the DSC and SA compared to the RAU-Net. It indicates that although the algorithm may have a slight oversegmentation phenomenon, the recall rate for the real organ area and the prediction accuracy for the organ area is higher. This is closely related to the ROI patch extraction based on bounding box regression prediction performed by the algorithm before organ segmentation. Therefore, it also fully illustrates that the segmentation idea of using a cascaded dual attention mechanism is also worthy of respect. In addition, in order to further intuitively present the segmentation performance of the algorithm, we visually displayed and compared the predicted segmentation and ground truth of the above four algorithms. The comparison results are shown in Figure 10, where the red line marks the actual contour of the organ, the yellow line marks the outline of the predicted segmentation result obtained by the algorithms, and the fourth row is the segmentation result after ROI cropping.