# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### Applications and Trends of GANs in Histopathological Image Segmentation  
The application of generative adversarial networks in medical image analysis has gained significant momentum in recent years due to their ability to synthesize high-quality, realistic data and enhance image characteristics across various clinical tasks. In histopathological image processing, GANs have been particularly valuable for tasks such as data augmentation, stain normalization, and image-to-image translation, offering robust solutions to data scarcity and variability challenges [51, 55]. Furthermore, segmentation tasks have also benefited from GAN-based approaches, which often incorporate adversarial losses to refine boundary accuracy and structural consistency [50]. The following figures provide a quantitative overview of how GANs have been utilized across different segmentation tasks and target regions in histological imaging.  
Figures 3 and 4 provide a detailed quantitative overview of the distribution of GAN-related research in histological image segmentation. As illustrated in Figure 3, the most frequently addressed task using GANs was data augmentation, appearing in 20 studies,  
Figure 2: PRISMA flow diagram containing selection, evaluation and inclusion of studies.  
followed by stain normalization with 12 studies. Other relevant applications included virtual staining, color normalization, and synthetic image generation, each represented in 4 studies. Less frequently explored were domain adaptation and reconstruction. This distribution highlights a clear research emphasis on preprocessing and data enrichment tasks, particularly those aimed at improving dataset variability and quality in histopathological imaging.  
Figure 4 categorizes the segmented regions targeted by these studies. The majority focused on nuclei segmentation, accounting for 20 studies, indicating a strong interest in cellular-level analysis. Segmentation of combined structures such as tissue and cells (8 studies), tissue alone (6 studies), and tissue and nuclei (2 studies) was also present, reflecting the complexity and layered structure of histological data. Fewer works addressed cell-only segmentation (1 study), cell and nuclei combined (2 studies), and the segmentation of area, count, and distance between cells (1 study). These findings suggest that nuclei-focused segmentation remains a priority, while more integrative or structural analyses involving multiple tissue components are less commonly pursued.  
Together, these results reinforce the role of GANs as  
through early 2025, a total of 41 publications met the selection criteria. The data indicate a growing interest in leveraging GANs for histopathology applications, starting with a single publication in 2018, gradually increasing to 3 in 2019 and 2 in 2020. A more pronounced growth can be observed from 2021 onward, with 6 publications in 2021, 9 in 2022, and peaking at 10 in 2023. Although there was a slight decrease in 2024 (7 publications) and 2025 (3 publications as of the analysis date), the trend overall reflects the expanding role of GANs in supporting histological image processing tasks such as stain normalization, synthetic image generation, and segmentation. This upward trajectory underscores the increasing adoption of GANs as powerful tools to enhance diagnostic workflows, improve image quality, and support data augmentation in histopathological research.