# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
###### Abstract
Their ability to simulate consistent, high-quality training data and adapt models to new domains directly addresses key limitations associated with manual staining and imaging inconsistencies, contributing to more robust and accurate segmentation in histological image analysis. However, despite their potential, GANs in particular present significant challenges that may limit their practical adoption in histological imaging workflows. These include the need for large amounts of training data, sensitivity to hyperparameters, and the lack of standardized evaluation metrics to assess the quality of generated outputs [9]. In order to gain a comprehensive understanding of GANs and their applications in image segmentation, a systematic review is necessary to consolidate current knowledge, evaluate the methodologies employed, and identify trends, limitations, and gaps in the literature. This review summarizes published articles on GAN-based architectures for histological image segmentation. The reviewed studies are categorized according to the specific tasks addressed by the GANs, such as data augmentation, stain normalization, virtual stain, and tasks addressed by the segmentation in organs, including breast, kidney, liver, stomach, prostate, and other regions. Therefore, this review study is expected to make valuable contributions by guiding researchers in selecting relevant research topics within this broad domain and by supporting the development of more effective methodological approaches. Although the use of Generative Adversarial Networks (GANs) is not new and has been explored in several review studies from different perspective [10; 11; 12], existing reviews do not comprehensively address the application of GANs to histological images. This gap can pose challenges for researchers entering this field. Thus, this systematic literature review aims to fill a significant research gap by providing a focused and thorough analysis of GAN-based approaches in the context of histological imaging. The main contributions of this article can be summarized as follows:* A thorough synthesis of the current state of the art regarding the use of generative models, particularly GANs, in histological image segmentation;
* Important perspectives on the use of GANs across different aspects, including yearly trends in GAN applications, the specific tasks addressed, the organs involved, the datasets utilized, and the evaluation criteria employed in the studies analyzed;
* A practical guidance for future research, helping to shape the direction of generative model development in histological imaging. The remainder of this article is organized as follows: Section 2 discusses previous literature reviews in related research domains, aiming to identify existing gaps and trace the field's evolution. Section 3 introduces the fundamental principles of histological image segmentation and explains the operational concepts of GANs. The review methodology, including the research questions, search strategy, and study selection criteria, is detailed in Section 4. Section 5 presents the review's main findings, such as standard evaluation metrics for segmentation models and the specific contributions of GANs in medical image segmentation. Section 6 discusses the implications of the results, offering a critical analysis of the reviewed literature. Finally, Section 7 presents the study's findings and the references used throughout the article.