# Mask Guidance Pyramid Network for Overlapping Cervical Cell Edge Detection
## 4 Results
### Results and Comparison
#### 4.4.2 Comparison with State-of-the-Art Algorithms  
In this part, we conduct comparative experiments on several representative algorithms, including RCF [31], ENDE [32], UNet++ [33], DexiNed [34], FINED [35], PiDiNet [36], and MSU-Net [37]. To comprehensively analyze the performance of these algorithms, we conducted quantitative and qualitative experiments. To make the comparative experiment fair under the same conditions, we implemented all of the comparison methods and evaluated them on the CCEDD corrected labelled datasets by MSU-Net with their higher accuracy than no corrected. The tested data use the same experimental settings, such as data augmentation methods, operation system and hardware environments. We present our model performance and results for other methods in Table 2. From the experimental results we can find that the proposed algorithm is superior to other algorithms on different datasets, and all the three evaluation metrics have considerable margin. In terms of test data, MGP-Net outperformed the previous best method by 0.8%, 0.4% and 1.3 respectively.  
To observe the edge detection performance of the proposed algorithm more intuitively, we report the qualitative test results in Figure 5. The qualitative results show the edge detection results of different algorithms in CCEDD datasets. The characteristic of CCEDD datasets is that it includes many challenging cases of irregular overlapping cervical cells, which brings difficulties to the general edge detection algorithm. Besides, the extremely low contrast between foreground and background organizations may increase the probability of inaccurate edge detection.  
For easy comparison, we convert our edge visualization results to binary images. From the experimental results, we can find that the classical RCF cannot deal with the complex situation of cell superposition due to the inherent limitations of its architecture. U-Net++is superior to RCF because it uses residual technology to effectively combine image features and can use more image information. But as shown in the third column in Figure 5, UNet++ is also not applicable to edge detection in the case of overlapping cells. The main reason why we analyze this phenomenon is that it does not have sufficient global receptive field and context information.  
Our MGP-Net detects more edge pixels through the proposed module and achieves satisfactory performance compared to other existing methods. By gradually fusing context information in multiple ranges and guiding according to the mask guidance of individual cells, features such as different shapes of cells at multiple scales can be effectively extracted.  
The interference of non-marginal pixels can be effectively suppressed, so that the network can learn more useful and discriminating features. In addition, the MGM-based mask guidance mechanism and RAM multi-level cascading feature aggregation strategy also help to obtain accurate edge prediction and effectively obtain the final edge detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AP** & **ODS** & **OIS** \\ \hline RCF & 0.667 & 0.638 & 0.645 \\ ENDE & 0.733 & 0.682 & 0.691 \\ UNet++ & 0.755 & 0.691 & 0.701 \\ DexiNed & 0.723 & 0.671 & 0.680 \\ FINED & 0.703 & 0.660 & 0.621 \\ PiDiNet & 0.648 & 0.624 & 0.628 \\ MSU-Net & 0.749 & 0.689 & 0.699 \\ MGP-Net & 0.763 & 0.695 & 0.714 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results comparisons of our method.