# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
###### Abstract
Recently, U-shaped network structures have been widely used in medical image processing [9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. U-Net [18], and its latest extensions, such as Transunet [13], have been the leading medical image segmentation methods in recent years. U-Net, combined with the Transformer [19], has achieved good performance in several medical image tasks; however, in the cervical cell segmentation task, the Transformer is not well applied to this task due to the limitation of data volume [20]. Some works enhance the segmentation performance of classical U-Net networks by designing complex aggregated feature modules between the backbone and segmentation semantic headers, including multi-scale feature integration [21] and multi-level feature integration [10], and moreover, enhanced channel and spatial representations [22; 23]. However, both of them do not consider feature differences and information dependency comprehensively, and cannot fully and effectively utilize mixed domain information and contextual information. Figure 1: The two columns on the left are normal cervical cells and the three columns on the right are infected cervical cells. Normal cervical cells have tiny and compact nuclei. When cervical cells are infected, the nuclei first appear abnormal in shape, color, and texture, among other features. Deep learning has been highly successful for computer vision tasks [24], and the ability of network feature extraction is the basis for a variety of downstream tasks. In this study, a network module for contextual information modeling and features refinement based on global dependency and local attention (GDLA) is proposed. For the closed and unique nature of cell nuclei, the global dependency module models contextual information to capture the a priori knowledge of cervical cells. For boundary segmentation challenges, a spatial attention module combined with contextual information is constructed for extracting boundary information to 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. Specifically, the network consists of three main components: the global dependency module, the spatial attention module, and the channel attention module. The global dependency module first uses the weighted average of all location features on the spatial domain of the feature map as the global context features, and then aggregates the global context features to each location feature to enhance the representation of each location feature. The spatial attention module and the channel attention module compress the channel dimension and the spatial dimension, respectively, and use the sigmoid function to activate to get the spatial attention map and the channel attention map. The three modules are computed in parallel and finally applied to the original feature map to complete the adaption of the input information. The main contributions are summarized as follows:  
* A module based on global dependency and local attention for contextual information modeling and feature refinement is proposed, based on a comprehensive consideration of feature differences and information dependency, which can be used as a basic component of the network to effectively enhance feature representation and improve the network's ability to extract and utilize features. * Considering information dependency, as well as the closure and uniqueness of the nuclei, the global context is modeled using long-range dependency. * A hybrid attention module is constructed to provide adaptive input information and enhance key information representation to suppress useless information. Among them, the spatial attention module extracts spatial detail information sufficiently and effectively to better refine the target boundaries. * An improved U-Net network, which implements an end-to-end network training model, is evaluated on the Herlev dataset to show that the segmentation performance can be steadily improved.