  This study introduces an innovative application of Conditional Generative
Adversarial Networks (C-GAN) integrated with Stacked Hourglass Networks (SHGN)
aimed at enhancing image segmentation, particularly in the challenging
environment of medical imaging. We address the problem of overfitting, common
in deep learning models applied to complex imaging datasets, by augmenting data
through rotation and scaling. A hybrid loss function combining L1 and L2
reconstruction losses, enriched with adversarial training, is introduced to
refine segmentation processes in intravascular ultrasound (IVUS) imaging. Our
approach is unique in its capacity to accurately delineate distinct regions
within medical images, such as tissue boundaries and vascular structures,
without extensive reliance on domain-specific knowledge. The algorithm was
evaluated using a standard medical image library, showing superior performance
metrics compared to existing methods, thereby demonstrating its potential in
enhancing automated medical diagnostics through deep learning
