# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
###### Abstract  
The refined segmentation of nuclei and the cytoplasm is the most challenging task in the automation of cervical cell screening. The U-Shape network structure has demonstrated great superiority in the field of biomedical imaging. However, the classical U-Net network cannot effectively utilize mixed domain information and contextual information, and fails to achieve satisfactory results in this task. To address the above problems, a module based on global dependency and local attention (GDLA) for contextual information modeling and features refinement, is proposed in this study. It consists of three components computed in parallel, which are the global dependency module, the spatial attention module, and the channel attention module. The global dependency module models global contextual information to capture a priori knowledge of cervical cells, such as the positional dependence of the nuclei and cytoplasm, and the closure and uniqueness of the nuclei. The spatial attention module combines contextual information to extract cell boundary information and refine target boundaries. The channel and spatial attention modules are used to provide adaption of the input information, and make it easy to identify subtle but dominant differences of similar objects. Comparative and ablation experiments are conducted on the Herlev dataset, and the experimental results demonstrate the effectiveness of the proposed method, which surpasses the most popular existing channel attention, hybrid attention, and context networks in terms of the nuclei and cytoplasm segmentation metrics, achieving better segmentation performance than most previous advanced methods. cervical cell segmentation; global dependency; local attention; contextual information; feature refinement +
Footnote †: journal: Medical Image Computing  
**Critical Cell Segmentation Method Based on Global Dependency and Local Attention**  
**Critical Cell Segmentation Method Based on Global Dependency and Local Attentions**
The results are optimized using post-processing. Common methods include location-based prior, shape prior, Markov random field, graphical cut [6], edge detection operator, etc. Although this means can optimize the segmentation results, it divides the segmentation task into multiple stages and the detection process is very cumbersome. Another is to improve the segmentation performance by optimizing the network model, and there are relatively few research works of this type. References [7; 8] designed complex network models to improve segmentation accuracy, which can achieve end-to-end network training, but is accompanied by heavy computational overhead. In addition, it does not sufficiently take into account a priori knowledge of the location and shape of cervical cells, and the variability of normal and abnormal cells. Refined nuclear and cytoplasm segmentation is essential for automated cervical cell screening, because when cervical cells are infected, the shape, color, and texture of the nuclei are the first features to appear abnormal, and once the cytoplasmic boundary is located, quantitative evaluation index values such as cell diameter and nucleoplasm ratio can be calculated. As shown in Figure 1, the size, shape, and staining of cervical cells were highly variable. The nuclei of normal cervical cells are tiny, compact, and uniformly colored, while the nuclei of infected cervical cells are swollen and irregular, with blurred borders, low contrast between the target and the background, and significant color differences within the nuclei, making them highly susceptible to false detection. For cervical cells, it is a basic a priori knowledge that the nuclei is only present inside the cell and is closed and unique, regardless of whether the cervical cells are infected or not. The segmentation of cervical cells is a difficult task. To meet these challenges and to exploit the a priori knowledge, a high-performance network for extracting rich features from images is required.