# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 6 Discussion  
This systematic review investigated the growing application of GANs in histological image segmentation, aiming to answer a set of research questions ranging from the types of tissues and datasets used to the model architectures and evaluation metrics employed. The findings underscore the versatility of GANs in addressing the inherent challenges of histopathological image analysis, such as staining variability, data scarcity, and the need for accurate segmentation of complex structures. In addressing the research questions (RQ1 and RQ2), concerning histological tissues and datasets, the analysis reveals a wide diversity in the types of histological tissues used, reflecting the medical research focus on pathologies related to vital organ systems. Organs such as the kidney, breast, colon, lung, and prostate are frequently represented, highlighting their high clinical relevance. In addition, less commonly studied tissues--including the thymus, testis, pleura, mediastum, and adrenal gland--have also been investigated, indicating an effort to develop more generalizable models. Datasets such as BBBC-data, PanNuke, and MoNuSeg, which contain samples from multiple organs, are particularly valuable for training models with broader generalization capabilities. Regarding the datasets, a significant variation in size was observed, ranging from small collections of 30 to 100 images (e.g., CryoNuSeg or in-house datasets) to large-scale repositories containing tens or even hundreds of thousands of images (e.g., TCGA, KIRC, Kather). Image dimensions vary widely, from small patches (e.g., 256 \(\times\) 256 pixels) to whole slide images (WSIs), which can exceed 100,000 \(\times\) 100,000 pixels. Image magnification, most commonly 20\(\times\) or 40\(\times\), is a critical factor that directly affects resolution and the level of detail available, thereby impacting segmentation outcomes. Most datasets provide detailed annotations, which are essential for supervised learning tasks. However, some (e.g., KIRC, TCGA, MUC1) are released without comprehensive labeling, limiting their applicability to unsupervised or semi-supervised approaches or requiring additional, resource-intensive annotation efforts. The public availability of most datasets is a significant strength, fostering scientific reproducibility and advancing AI research in pathology, although access to specific datasets remains restricted. Regarding the research questions on the objectives of applying GANs and the architectures used (RQ3 and RQ4), the studies analyzed reveal various purposes and technical approaches, highlighting the versatility of these networks. The most prevalent application of GANs identified in the review is data augmentation, the most common use case reported in 20 studies. In this context, GAN architectures such as CycleGAN, PGGAN, cGAN, and WGAN-GP synthesize realistic histological images, enriching training datasets and enhancing the robustness and adaptability of segmentation models. The second most frequent application is stain normalization, essential for mitigating the bias introduced by staining variability. Models like CycleGAN, Pix2 Pix GAN (P2P-GAN), DSTGAN, Restore-GAN, WGAN-GP, cGAN, and GramGAN have been widely used to harmonize the visual appearance of images while preserving their underlying morphological structure. Following this, color normalization, although overlapping with stain normalization, aims explicitly to adjust color variations caused by different scanners and acquisition settings, directly influencing analytical consistency and segmentation accuracy [102,103]. Another important application is virtual staining, which simulates histological staining on unlabeled images, enabling faster and more cost-effective analyses while preserving the original tissue samples [104]. Architectures such as CycleGAN, E-CUT, GramGAN, CSTN, HistoStarGAN, Pix2 Pix, and CUT have been employed for this purpose.