# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
###### Abstract  
Histological image analysis plays a crucial role in understanding and diagnosing various diseases, but manually segmenting these images is often complex, time-consuming, and heavily reliant on expert knowledge. Generative adversarial networks (GANs) have emerged as promising tools to assist in this task, enhancing the accuracy and efficiency of segmentation in histological images. This systematic literature review aims to explore how GANs have been utilized for segmentation in this field, highlighting the latest trends, key challenges, and opportunities for future research. The review was conducted across multiple digital libraries, including IEEE, Springer, Scopus, MDPI, and PubMed, with combinations of the keywords \"generative adversarial network\" or \"GAN\", \"segmentation\" or \"image segmentation\" or \"semantic segmentation\", and \"histology\" or \"histological\" or \"histopathology\" or \"histopathological\". We reviewed 41 GAN-based histological image segmentation articles published between December 2014 and February 2025. We summarized and analyzed these papers based on the segmentation regions, datasets, GAN tasks, segmentation tasks, and commonly used metrics. Additionally, we discussed advantages, challenges, and future research directions. The analyzed studies demonstrated the versatility of GANs in handling challenges like stain variability, multi-task segmentation, and data scarcity--all crucial challenges in the analysis of histopathological images. Nevertheless, the field still faces important challenges, such as the need for standardized datasets, robust evaluation metrics, and better generalization across diverse tissues and conditions. generative adversarial network; medical image analysis; segmentation; histological image; deep learning 20251578020202515780  
During histopathological evaluation, hematoxylinlin and eosin (H&E) staining is widely used to highlight cellular and tissue structures. Hematoxylin stains cell nuclei blue or purple, while eosin colors the cytoplasm and extracellular matrix in shades of pink [3]. Although effective, the staining process is highly sensitive to variations in protocols, reagent concentrations, and incubation times, which can result in considerable differences in color and contrast between samples. In addition, image acquisition may be affected by differences in slide scanners, lighting conditions, and resolution settings [4]. These sources of variability can lead to inconsistencies in visual appearance, making automated analysis, especially tasks like segmentation, more challenging and less generalizable across datasets. In response to the challenges posed by variability in staining and image acquisition, generative neural networks, such as generative adversarial networks (GANs), have emerged as promising tools in the field. These models are capable of learning complex data distributions and producing realistic synthetic representations, making them particularly valuable for enhancing model generalization across heterogeneous datasets [5]. Building upon the growing interest in the use of GANs for histological image segmentation, recent research has explored their integration not only as primary segmentation models but also in auxiliary roles to address specific limitations in medical imaging workflows. Generative networks have been employed for tasks such as data augmentation [6], domain adaptation [7], and label refinement [8]. For example, [PERSON] et al. [6] leveraged GANs to synthesize realistic histopathological patches, enabling improved segmentation performance in datasets with limited annotations. In the context of domain adaptation, [PERSON] and [PERSON] [7] proposed an edge-preserving GAN to bridge domain gaps caused by differences in staining or scanning protocols, thus ensuring consistent segmentation across diverse datasets. Furthermore, [PERSON] et al. [8] demonstrated how GANs can refine weak annotations by learning the underlying structural distribution of histological components.