## 2 Related Work  
With the rapid development of computer vision techniques, deep learning semantic segmentation methods have been widely applied in various fields. Semantic segmentation technology has been applied in agricultural areas such as crop pest and disease leaf segmentation [10], soil clad segmentation in farmland images [11], and locating pruning points of apple trees [12]. In medical image analysis, semantic segmentation technology has also shown great potential. The encoder-decoder architecture of U-Net achieved an outstanding segmentation performance on medical cell datasets [13]. The network realized multi-scale feature fusion through four skip connections, achieving remarkable segmentation results even with only 30 training images in the cell dataset. Apart from U-Net, semantic segmentation architectures, such as DeepLabV3+ [14], with its atrous spatial pyramid pooling (ASPP) module and multi-scale design, have also achieved excellent results. U-Net excels at capturing fine-grained target boundary information in images, while DeepLabV3+ is more suitable for capturing multi-scale semantic context information. UNet++ [15] improved the long connections in UNet, combining long and short connections, enabling the model to benefit from high-level semantic information from long connections and gradient backpropagation optimization from short connections. ResUnet [16] introduced residual connections into the UNet structure, improving gradient propagation and alleviating gradient vanishing and semantic information loss. In addition to these classic convolutional neural network semantic segmentation architectures, Generative Adversarial Networks (GANs) have gradually been applied to image segmentation tasks. [PERSON] [17] first applied GANs to semantic segmentation by replacing the generator with the Dilated8 module and proposing a way to process adversarial network input by multiplying the label or segmentation network output with the original image to form a multi-channel input. [PERSON] [18] incorporated depthwise separable convolutions in the discriminator for efficiency and adopted the Nadam [19] optimization algorithm to accelerate loss function convergence. The ability of CycleGAN [20] to perform pairwise image style transfer has also been used for semantic segmentation, such as the semi-supervised CycleGAN semantic segmentation network proposed by [PERSON] et al. [10], which leverages cycle consistency constraints to learn bidirectional mappings between unpaired images and segmentation masks. Although the exploration of GANs in semantic segmentation is limited, their promising performance in segmentation tasks demonstrates the great potential of GANs in image segmentation [21]. Although GANs have certain advantages in semantic segmentation tasks, the generator's capabilities limit their segmentation performance. GANs can optimize generator parameters only when the images are high quality. The earliest semantic segmentation GAN networks used U-Net as the generator, and researchers have since made various improvements based on U-Net. For example, [PERSON] et al. [22] proposed a task-driven GAN-based image segmentation algorithm using U-Net as the generator. At the same time, the discriminator employed a multi-scale classification network [23] with different receptive fields to guide the model in generating more detailed information. [PERSON] et al. [24] used DenseU-Net [25], containing Inception [26] modules as the GAN generator, achieving a seamless fusion of features, from shallow to deep layers. Although these methods improved network segmentation accuracy to some extent, the convolutional neural network (CNN)-based U-Net encoder and decoder are limited by the inherent receptive field of convolutional kernels, allowing only local neighborhood information to be captured, and failing to adaptively capture long-range dependencies in the input content [27]. The long-range modeling capability must be improved for tasks such as cell segmentation [28; 29; 30], leading to semantic information loss.