## 4 Discussion  
We can conclude from the analysis of the experimental results that the reasons for the success of the method proposed in this article can be summarized by the following two points. First, according to the different convolution based feature detection (of different channels) steps, the feature map with strong edges is extracted by controlling the scale size. Finally, the fusion module with strong semantic \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline Mask R-CNN & 89.57 \\ Our & 91.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Target area border average accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **DSC** & **SP** & **SN** & **MAD (mm)** & **HD (mm)** & **RSD (10\({}^{-2}\))** & **Time(s)** \\ \hline ASM & 0.9680 & 0.9884 & 0.9694 & 0.352 & 1.140 & 4.259 & 1.976 \\ FCN [14] & 0.8828 & 0.9275 & 0.8981 & 0.423 & 1.229 & 10.574 & 0.462 \\ U-Net [15] & 0.8953 & 0.9395 & 0.9069 & 0.405 & 1.220 & 9.636 & 0.483 \\ SegNet [16] & 0.9072 & 0.9501 & 0.9112 & 0.401 & 1.217 & 8.344 & 0.497 \\ Deeplab v3 [17] & 0.9439 & 0.9762 & 0.9386 & 0.372 & 1.185 & 6.015 & 0.348 \\ Mask R-CNN [8] & 0.9775 & 0.9974 & 0.9712 & 0.340 & 1.112 & 3.580 & 0.330 \\ Our & 0.9810 & 0.9983 & 0.9764 & 0.332 & 1.106 & 2.412 & 0.342 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation of different methods. Figure 7: Mask R-CNN and our method’s target region border average accuracy. features is generated via a fusion technique. Second, according to the statistics of the segmentation target size in the TRUS image, a more reasonable anchoring mechanism and the regression method of the target area are designed to obtain a more accurate target area, which effectively avoids the artifacts and adjacent tissues to produce negative segmentation effects. The segmentation result is shown in Figure 8.  
We can see (in Table 3) that the effect improvement between the segmentation results of FCN, U-Net, SegNet, and Deeplabv3 in the TURS image has a small margin. Compared with the aforementioned method, Mask R-CNN has greatly improved the key indicators DSC and MAD by 0.336 and 0.039 mm. In addition, from Table 2, Table 3, we can see that the method used in this paper has not only improved 0.135 and 0.022 mm in DSC and MAD indicators compared to Mask R-CNN. We can produce a more intuitive observation effect for different segmentation according to Figure 9. In all the segmentation methods in Figure 9, except Mask R-CNN and the method proposed in this paper, the remaining methods are not instance segmentation. This has no detection function for the segmentation target so that the non-prostate tissue area and the artifact area will have a certain effect on the segmentation result. First, the ASM method uses the prior shape to add to the weighted model and searches for the edge of the target by initializing the shape. Although this method will have a good effect on the segmentation results. But because of its long time-consuming nature, it cannot meet the clinical application. FCN lacks the consideration of global context information, so the segmentation result is the worst. U-Net and SegNet, due to multiple down-sampling and up-sampling, cause detailed information to be lost to varying degrees, resulting in unsatisfactory segmentation results. On the contrary, the Deeplab v3 model using feature fusion means it effectively avoids the loss of detailed information, so the segmentation results have better performance than the above four methods. Finally, due to the limitations of semantic segmentation on prostate image segmentation, this method does not show better performance in segmenting TRUS images. Compared with Mask R-CNN, which also has the function of instance segmentation. In the method proposed in this article, the average accuracy of the target area border has better performance.