# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### Segmentation Task in Histological Image Analysis
#### 5.5.1 Nuclei Segmentation
This framework not only performs nuclei segmentation but also classifies tissue types, leveraging shared features for both tasks. The model capitalizes on the semantic interdependencies between segmentation and classification, enhancing overall performance in complex tissue environments. Building upon this, [PERSON] et al. [80] proposed a multi-task GAN architecture that simultaneously learns nuclei segmentation and image reconstruction. Their model enhances spatial coherence and focuses on the most informative regions by integrating dual attention mechanisms and recurrent convolutional modules. Data augmentation and synthetic data generation represent other important applications of GANs. [PERSON] et al. [91] proposed generating annotated synthetic data from small labeled samples. Synthetic backgrounds are created using base image patches, with GANs preserving texture characteristics. Nuclear shapes are extracted from original images using ground truth masks and then transformed with image processing techniques. These shapes are placed on filtered backgrounds, maintaining nuclear morphology and distribution. The synthetic image patches, along with their corresponding masks, are used to train a modified U-net for nuclear region segmentation. [PERSON] et al. [77] conducted a comparative analysis involving staining normalization and data augmentation across two distinct tasks: generalization to images obtained in invisible pathology laboratories for mitosis detection and generalization to invisible organs for nuclei segmentation. GANs are also widely employed as supportive tools in histopathological image analysis. [PERSON] et al. [89] took this further by developing Restore-GAN, which improves image quality by restoring blurred regions, enhancing low resolution, and normalizing colors. When the Mask R-CNN segmentation model was applied to these restored images, its performance improved significantly. Similarly, [PERSON] et al. [81] demonstrated a custom CNN for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks, so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt), to facilitate cancer diagnosis in low-resource settings. [PERSON] et al. [78] proposed SENSE (SEmantic Nuclear Synthesis Emulator), a novel framework for synthesizing realistic histological images with precise control over cellular distributions. The approach introduces three main innovations: a statistical modeling system that captures class-specific nuclear features from specialized annotations, a hybrid ViT-Pix2 Pix GAN architecture that effectively translates semantic maps into high-fidelity histological images, and a modular design that allows independent control of cellular properties, including type, count, and spatial distribution. [PERSON] and [PERSON] [90] presented a new deep generative model (LSGAN) to reduce the color variation found among histological images. The proposed model assumes that the latent color appearance information, extracted through a color appearance encoder, and the constraint information, extracted via a patch density encoder, are independent of one another. The research demonstrated that the performance of nuclei segmentation improves on the image patch after color normalization compared to the image patch before normalization. Style augmentation using multi-modal microscopy data is the focus of [57]. The authors developed a technique that generates diverse training samples by simulating different imaging modalities. The concept of style augmentation is strongly connected to image-to-image translation models, such as the use of a GAN, which can simulate various staining conditions. These augmentations assist nuclei segmentation models in generalizing across datasets. Finally, [PERSON] et al.