# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 6 Discussion
Synthetic image generation, which involves creating artificial histological images from semantic maps or structural sketches, is another valuable application, especially in contexts where annotation is resource-intensive [105]. cGAN and ViT-P2P are examplesof models applied to this task. Lastly, domain transformation and high-resolution image reconstruction play a key role in overcoming dataset heterogeneity and improving image quality, facilitating cross-domain generalization and restoring fine structural details [106]. Notable models in these areas include CycleGAN and SRGAN-ResNeXt, respectively. The most frequently used GAN architectures in histological image processing include CycleGAN, which emerged as the most prominent model, especially for stain normalization and data augmentation, due to its ability to perform image-to-image translation without requiring paired data. Other prominent architectures include cGAN, Pix2 Pix, PGGAN, WGAN, and their variants, each tailored to address specific domain challenges. More recent and specialized models like DST-GAN, HistoStarGAN, and ViT-P2P indicate a trend toward customized GAN architectures designed for specific histopathological tasks. In addressing the research questions regarding the objectives of segmentation and the segmentation architectures employed (RQ6 and RQ7), this review shows that the primary goal of segmentation in histopathological images is to identify and delineate specific structures--such as cell nuclei, glands, collagen fibers, or tumor regions--in order to extract quantitative information that complements qualitative analysis. The most common segmentation task is nuclear segmentation, reported in 20 studies, due to the clinical relevance of nuclear morphology in cancer diagnosis and prognosis [107]. Models such as U-Net, Mask R-CNN, and their variants are frequently used, often enhanced by GAN-based techniques for data augmentation, stain normalization, or domain adaptation. Another relevant task is cell segmentation, essential for quantitatively analyzing cell populations and tumor features. Both supervised and unsupervised approaches are explored, often leveraging GANs for synthetic data generation or style enhancement. Tissue segmentation, which targets larger and more complex regions, addresses challenges such as staining variability and enhances model generalization. Common architectures used include U-Net, ResNet, and SegNet. In multiclass segmentation (e.g., tissues + nuclei or cells), some studies simultaneously segment multiple structures to provide a more comprehensive understanding of the tissue microenvironment, often using U-Net-based models and integrating GANs for data generation or enhancement. Due to their effectiveness in medical image segmentation, the dominant segmentation architectures remain U-Net and Mask R-CNN, particularly under data-limited scenarios. Other architectures such as CNN, ResNet, HoVer-Net, and SegNet are also mentioned and frequently integrated with or supported by GANs. In the research questions addressing GAN performance evaluation and segmentation quality assessment (RQ5 and RQ8), a distinct set of metrics is employed, each aligned with the specific goals of the respective model types. For GANs, the primary focus lies in visual fidelity and the statistical similarity between generated images and real data. The Frechet Inception Distance (FID) is one of the most commonly used metrics, as it measures the distance between feature distributions extracted from real and synthetic images, indicating how convincingly realistic the generated images are. The Structural Similarity Index (SSIM) evaluates structural fidelity, which is essential for preserving fine histological details. Additional metrics such as Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) are also employed, along with more specialized measures for color consistency, such as Between-image Color Constancy (BiCC) and Within-set Color Constancy (WsCC).