# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### Segmentation Task in Histological Image Analysis
#### 5.5.1 Nuclei Segmentation  
Nuclei segmentation is the most common task among the reviewed studies, as evidenced by the predominance of models specifically applied to this cellular structure. [PERSON] et al. [2018] developed an analysis pipeline that segments nuclei in whole-slide tissue images from multiple cancer types, utilizing a multi-level quality control process (WSI level and image patch level) to evaluate the quality of segmentation results. In [PERSON] et al. [2018], an application based on deep learning networks has been developed. The system performs image preprocessing and generates synthetic images to address the scarcity of pathological data through a CycleGAN network. The generated images are fed into CNN, Mask R-CNN, and modified U-Net networks to segment nuclear regions and separate overlapping kidney nuclei. The study by [PERSON] et al. [2018] addresses the challenging task of nuclei segmentation in histopathological images by proposing a Cross-Boosted Multi-Target Domain Adaptation (CB-MTDA) framework. This method tackles the significant issue of domain variability, which often obstructs the generalization of segmentation models across different staining protocols and imaging modalities. Unsupervised domain adaptation (UDA) methods have been proposed to mitigate the distributional gap between different imaging modalities for unsupervised nuclei segmentation in histopathology images ([PERSON] et al.) [2018]. Moreover, a dual-branch nucleus shape and structure-preserving module has been proposed to prevent nucleus over-generation and deformation in the synthesized images of liver cancer, kidney cancer, and colon cancer. [PERSON], [PERSON] and [PERSON] [2018] introduced a Generative Stain Augmentation Network (G-SAN) for stain augmentation of H&E-stained histological images. G-SAN can augment an input cell image with selected and realistic stains by disentangling the morphological and stain-related representations. Through the downstream tasks of patch classification and nucleus segmentation, it was quantitatively demonstrated that the quality of G-SAN-augmented images surpasses that of images produced by existing stain augmentation approaches. GANs are also widely employed as supportive tools in histopathological image analysis. For instance, [PERSON] and [PERSON] [2018] leveraged GANs to perform stain normalization via adversarial stain transfer. This pre-processing step mitigates variability in staining protocols and improves the consistency of nuclei segmentation algorithms. Similarly, [PERSON] et al. [88] introduced a deep adversarial training framework specifically designed for multi-organ nuclei segmentation. Their architecture employs a segmentation network guided by a discriminator that evaluates the morphological realism of the segmented masks. This adversarial learning paradigm improved generalization across different tissue types and staining variations. [PERSON] et al. [67] proposed a self-supervised pretraining method using CycleGANs to translate histological images between unpaired domains. The framework aims to capture nuclear morphology and information distribution by translating histopathological images and pseudo-masked images. While the approaches above rely on supervised learning, other studies explore unsupervised and semi-supervised paradigms. [PERSON] et al. [59] demonstrated the use of unified GANs with a novel loss formulation to perform robust cell-level visual representation learning in an unsupervised setting. The model is not only label-free and easily trained but also capable of unsupervised segmentation of bone marrow cellular components. [PERSON] et al. [79] explored full-nuclei segmentation by choosing only a few image patches to annotate, augmenting the training set of selected samples, and achieving nuclei segmentation in a semi-supervised manner. [PERSON] et al. [64] presented MASG-GAN, a novel architecture that combines multi-view attention and superpixel-guided mechanisms.