# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 2 Related Review Works
The review distinguishes itself by offering a well-structured analysis of application domains, model architectures, and performance outcomes, providing valuable insights into the evolution of GAN-based methods. Focusing specifically on image segmentation, [PERSON] et al. [23] presented an in-depth review of GAN-based methods and their architectures tailored for medical segmentation tasks. While the review covers many techniques, it does not systematically evaluate the datasets or compare performance across benchmarks, limiting its reproducibility utility. Unlike previous reviews that broadly address the use of GANs in general medical imaging or focus on specific applications such as data augmentation or stain normalization, this review provides a focused and structured analysis of GAN-based architectures, specifically within the context of histological image segmentation. It distinguishes itself by not only examining segmentation models but also exploring the auxiliary roles GANs play, such as domain adaptation and label refinement, within segmentation pipelines. Furthermore, the review categorizes studies according to both the technical tasks performed and the anatomical regions analyzed, offering a multidimensional perspective that is currently lacking in the literature. This level of granularity aims to support researchers and practitioners in identifying methodological gaps, understanding current trends, and selecting appropriate GAN approaches tailored to specific histological challenges. Table 1 presents a comparative analysis of recent and relevant studies in the literature focused on using generative adversarial networks for histological image segmentation. The study includes key aspects such as types of images addressed (including histopathological slides and whole slide images), datasets, investigated tasks, GAN models discussed, evaluation metrics, and objectives, which follows well-recognized standards to compare scientific works in SLR studies [24; 25]. Moreover, the selected metrics align with those commonly used in AI-driven imaging reviews across multiple domains. \begin{table}
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Overall, these studies underscore the increasing interest in GAN-based methods for enhancing analytical accuracy in complex tissue structures. They address critical challenges such as stain variability, scarcity of annotated data, and domain shifts across heterogeneous datasets. Based on the performed analyses, we could identify that while GANs have shown promising results in histological segmentation tasks, the current literature remains sparse and fragmented. Most reviews still concentrate on broader medical imaging domains (e.g., MRI, CT) and provide limited discussion on histology-specific applications. This reveals a clear gap in domain-focused synthesis, particularly concerning validating GANs. As presented in this study, a focused, systematic review is thus essential for understanding the capabilities, limitations, and emerging directions of GANs in histological image segmentation. This review article focuses exclusively on using GANs for the \"image segmentation task\" in image histology. It examines papers published from GAN's introduction in 2014 to February 2025. Although the analysis may not have been exhaustive, these studies are expected to provide sufficient knowledge to aid both novice and experienced researchers in selecting research topics and refining methodological approaches within the studied subject.