Decoupled SculptorGAN Framework for 3D Reconstruction and Enhanced Segmentation of Kidney Tumors in CT Images

Our proposed work, SculptorGAN, represents a novel advancement in the domain of medical imaging, for the accurate and automatic diagnosis of renal tumors, using the techniques and principles of Generative Adversarial Network (GAN). This dichotomous framework forms a contrast to the normal segmentation models like that of U-Net model but, instead, founded on a strategy that is aimed towards reconstruction and segmentation of CT images, particularly of renal malignancies. The core of the SculptorGAN methodology is a GAN-based approach for precise three-dimensional rendering of renal anatomies from CT scans, followed by a segmentation phase to correctly separate the neoplastic from non-neoplastic tissues. In fact, SculptorGAN was designed to circumvent limitations that come as inherent in the segmentation techniques, and in this case to eliminate them. In fact, by including such an advanced algorithmic architecture, accuracy of diagnosis in SculptorGAN has increased to 96.5%, which is the primary aspect behind early detection and thus proper curing of renal tumors. The better results were ascribed to more accurate and detailed reconstruction of renal structures that the framework allowed, apart from the better segmentation. The performance analyses show quantitative results with respect to the presented datasets, while the validation shows that SculptorGAN outperforms most of the traditional models such as U-Net. In particular, SculptorGAN decreased the time taken for 3D reconstruction by about 35% while increasing the accuracy of segmentation by 20% or more. The outcome, in their turn, may suggest this improvement in efficiency and the level of reliability for renal tumor diagnosis as of having far-reaching implications for the patient treatment and its outcomes. In conclusion, the framework deals with all the challenges with an accurate diagnosis of renal tumors and brings betterment in the overall field of medical image analysis by providing the abilities of GANs for the betterment in image reconstruction and segmentation.