# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 6 Discussion
For segmentation models, the evaluation prioritizes the accuracy in identifying and delineating structures. The Dice Similarity Coefficient (DSC) is the primary metric, quantifying the overlap between predicted and annotated segmentations and is particularly critical in tasks involving nuclear segmentation. Complementary metrics such as F1-score, precision, and recall are commonly used to assess the model's ability to handle false positives and false negatives. The Intersection over Union (IoU) offers a robust measure of segmentation precision by comparing predicted and ground truth masks. Additionally, spatial metrics like the Hausdorff Distance (HD), which calculates the most significant distance between corresponding boundary points, are essential when the shape and positioning of segmented nuclei have clinical implications. While the metrics used for GANs and segmentation focus on different aspects, there is an increasing need to consider them in a complementary fashion, particularly in hybrid approaches where synthetic images are used to improve the training of segmentation models or where segmentation maps guide image generation. The growing interest in GAN-based approaches is primarily driven by their ability to synthesize realistic images, address data scarcity issues, and facilitate tasks such as stain normalization, data augmentation, and domain adaptation. Several studies have demonstrated the potential of GANs in improving segmentation accuracy, especially in challenging scenarios involving small datasets, class imbalance, and variations in staining protocols. These contributions have enabled progress in key segmentation tasks, including nuclei, cellular, and tissue segmentation, ultimately supporting downstream applications such as tumor detection, quantification of cellular structures, and prognostic assessments. Current research trends reveal a strong focus on developing hybrid models that integrate GANs with other architectures, such as U-Net, attention mechanisms, and transformers, to enhance the accuracy and generalization of segmentation models. Moreover, there is an increasing interest in exploring self-supervised and weakly supervised learning strategies, aiming to reduce the dependency on costly pixel-level annotations. The integration of multi-modal data, such as combining histological images with clinical or genomic information, is also emerging as a promising avenue to support holistic analysis and improve clinical decision-making. Besides exploring technical and methodological aspects, it is important to consider the ethical and clinical implications of using GAN-based approaches in histopathology. Creating synthetic data, while helpful for training models and improving generalization, also risks introducing artifacts that may not match real biological structures, which could lead to misinterpretations or diagnostic mistakes. Therefore, thorough validation of these models in clinical settings is essential to guarantee reliability and patient safety. Additionally, incorporating GAN-based tools into clinical workflows involves navigating complex regulatory frameworks that often lag behind technological progress. Addressing these issues requires not only technological innovation but also collaboration across disciplines with pathologists, ethicists, and regulatory agencies to ensure AI solutions meet healthcare standards and ethical responsibilities.