# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 2 Related Review Works  
While systematic literature reviews are widely recognized as essential for understanding a research domain, it is equally important to identify prior studies with similar objectives. Doing so helps uncover existing gaps in the literature and provides insight into how the field has evolved. This section presents previous literature reviews in related areas, serving as a foundation to support the discussions throughout this article. In recent years, medical image segmentation has significantly transformed from traditional rule-based algorithms to more advanced deep learning-based approaches. In particular, histological image segmentation, a domain characterized by complex tissue structures, staining variability, and subtle morphological patterns, has greatly benefited from data-driven methods. These modern techniques address challenges such as low contrast, staining artifacts, and overlapping cellular structures, enabling more accurate and robust segmentation of tissue components and cellular features [13; 14; 15]. Among the most promising approaches are GANs, which have demonstrated significant potential in histopathological image segmentation. GANs operate through adversarial training, allowing them to model complex distributions and generate realistic segmentation outputs even when working with limited or imbalanced datasets. One of the key advantages of GANs in this domain is their ability to handle domain adaptation and data bias by learning the underlying data distribution in a supervised, semi-supervised, or unsupervised fashion [16; 17]. Several studies have comprehensively reviewed applying GANs in medical imaging, highlighting their promise and limitations. For instance, [PERSON] et al. [16], [PERSON] et al. [18] focused on the role of GANs in medical image segmentation, especially emphasizing their potential to improve image quality and structure delineation. The review covers various medical imaging modalities without specializing in histological images. The article is helpful as a general foundation but does not delve into critical aspects such as histopathology-specific challenges, staining heterogeneity, or cellular-level image characteristics. Segmentation is conceptualized, and the authors do not conduct a systematic analysis of datasets or performance metrics. In contrast, [PERSON] et al. [19] delivers a comprehensive review of GAN applications across the medical imaging domain, covering tasks such as synthesis, augmentation, enhancement, and segmentation. However, segmentation is only treated as one of several applications, and histological images are not a central concern. From a theoretical perspective, [PERSON] et al. [17] offers an in-depth review of GAN algorithms, models, and training strategies. While this work is essential for understanding foundational and architectural aspects of GANs, including advanced variants such as PGGAN and WGAN, it does not focus on medical imaging tasks and completely omits segmentation-specific discussions. Therefore, it serves as a firm technical reference but does not bridge the gap toward domain application. Specific to histopathology, [PERSON] et al. [20] explored using GANs in digital pathology, focusing on whole-slide imaging and nuclear segmentation. This work highlights the variability of histological data and the potential of GANs to normalize and adapt images across domains. Nonetheless, it provides limited quantitative analysis and no comparison of GAN variants. Other works like [PERSON] et al. [5] and [PERSON] et al. [21] centered on data augmentation and attention mechanisms, respectively. While they broaden the understanding of GAN applications, they generally do not address regulatory or ethical implications. A more recent and comprehensive contribution by [PERSON] et al. [22] approached the use of GANs from the perspective of medical image reconstruction while also considering segmentation as part of the broader landscape of image enhancement tasks.