## 1 Introduction  
Microscopic imaging techniques are an indispensable component of biological research [1]. Cells, as the fundamental units of life, contain a wealth of biological information. The application of confocal microscopy [2] and single-molecule techniques [3] for cellular image analysis, enabling precise monitoring of cell size and morphology, holds profound significance for advancing our understanding of various aspects of life sciences. These advanced technological approaches provide powerful tools for elucidating intracellular structures and functions, thereby driving progress in life science research. Traditionally, the segmentation of cell images required manual processing. However, images obtained under different lighting and observation conditions often exhibit high noise [4], blurred boundaries, and stacked, adhered cells, posing challenges for segmentation and necessitating the involvement of professionals. Computational vision techniques for segmenting microscopic cell images enable rapid and accurate image processing and analysis. Compared to traditional manual observation, these techniques allow for large-scale, automated data acquisition and analysis, significantly improving work efficiency. Fine-grained analysis can be achieved through detailed image processing and feature extraction of microscopic cell images, and the obtained cell data can be used for long-term research, and can provide data support for biological modeling [5].  
In machine vision, medical image segmentation algorithms can be broadly categorized into unsupervised and supervised segmentation. Unsupervised segmentation methods include threshold-based, region-based, and edge-based approaches [6], relying solely on image brightness and color information. Among these, the threshold method is a non-contextual segmentation technique [7] that classifies images based on grayscale values, dividing them into two categories. Region-growing methods determine growth points and aggregate pixels or sub-regions into more significant regions. On the other hand, edge-based segmentation methods determine edges by thresholding the image gradient [7]. These unsupervised methods perform well when edges are transparent, but can be susceptible to light and noise, leading to missing boundaries when noise and artifacts are present. In contrast, supervised segmentation methods, such as probabilistic graphical models and statistical shape models, can better capture organ shapes and produce more accurate results [6].  
With the rapid advancement of computational capabilities and the growth of deep learning technologies, deep learning-based image segmentation methods have emerged as the predominant approach in cellular recognition. For instance, DeepCell has efficiently segmented large-scale cellular data [8]. At the same time, Cellpose, leveraging a UNet backbone network, has had good results in the semantic segmentation of cellular images [9]. Nevertheless, the precise computer-based recognition of biological cell microscopy images remains a formidable challenge. This is primarily due to the multifaceted influences of imaging equipment performance, photobleaching effects, and the intricate morphological features of cells at the microscopic level. This study proposes a CellGAN semantic segmentation network with a Feature Completion Mechanism based on a generative adversarial network for cell images to address this issue. The main contributions include the following:  
1. Constructing a feature enhancement generator that incorporates a Transformer to supplement long-range semantic information;
2. Introducing bilinear interpolation for feature completion in the self-attention module to reduce computational complexity and enhance inference speed;
3. Employing two-dimensional positional encoding in the self-attention mechanism to supplement positional information and facilitate position recovery;
4. Integrating segmentation loss and discriminator error for a generator accuracy correction strategy to optimize segmentation accuracy continuously.