# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
## 4 Experiment
### Comparison Experiments  
The performance differences between the proposed method and the mainstream channel attention network [41], hybrid attention network [42], and contextual network [38] for cervical cell nuclei and cytoplasm segmentation were compared in the first place.  
The performance of the modules was comparatively compared by embedding GC (Global Context) Block [38], SE Block [41], CBAM Block [42], and GDLA Block in U-Net, where GC, SE, and CBAM have been described in Section 2. Table 1 shows that the proposed method outperforms the other three networks, both for nuclei segmentation and cytoplasm segmentation. This is due to the fact that our proposed method integrates local feature differences and global information dependency, and effectively captures the closure and uniqueness of nuclei, refines cell boundaries, and provides adaptive input information by modeling contextual information and refining features. Compared with the baseline, GDLA has a higher performance (approximately 3.8% \(\uparrow\) on ZSI of nuclei and 3.2% \(\uparrow\) on ZSI of cytoplasm) with a slight increase in #params. Figure 5 shows the visual results of the effect of different modules. In Figure 5, the bar graph indicates the results of evaluation metrics for nucleus segmentation, and the line graph indicates the results of evaluation metrics for cytoplasm segmentation.  
Figure 5: Visual results of the effect of different modules.  
Moreover, the proposed GDLA is compared with the dedicated networks on nucleus segmentation metrics, since most of the dedicated networks target only the segmentation of cervical nuclei. Table 2 shows the experimental results of several methods that have been specially designed for extracting the features of cervical cells on the Herlev dataset. The results are all from the original papers [2, 7, 8, 26, 27, 29, 30, 32, 45]. As can be seen, the proposed method achieves the competitive ZSI score on cervical cell nuclei segmentation and demonstrates an outstanding segmentation performance on the Herlev dataset. Among them, although Gap-search MRF and PUG-Net have achieved comparable results to the proposed method, the former uses the traditional method of Markov random field for the pixel-level classification of cervical cell images, which divides the cervical cell segmentation task into multiple steps and makes the process tedious and complicated. The method uses a non-local mean filter to attenuate the \"noise\", generates superpixels, and manually extracts a 13-dimensional feature vector for each superpixel. Finally, a solving algorithm is required. There is instability in long assembly lines and complex processes. Any segmentation errors in the intermediate steps can lead to the failure of the entire segmentation process. The latter, both reference [7] and reference [8], on the other hand, although using a deep learning approach, utilizes the idea of the model ensemble to incrementally train the neural network. Furthermore, although reference [32] achieves the highest ZSI score on cervical cell nuclei segmentation, its method only focuses on singlet nuclei segmentation, meaning that Mask-RCNN is utilized to locate the nuclei before segmentation, which to some extent excludes background interference while ignoring the important qualitative and quantitative indicators of cervical cell characteristics such as the nucleoplasmic ratio. Compared to these methods, the proposed approach is more simple, efficient, and elegant.