Advancements in Automatic Kidney Segmentation Using Deep Learning Frameworks and Volumetric Segmentation Techniques for CT Imaging: A Review

The efficiency of Three-Dimensional Convolutional Neural Networks (3-D-CNNs) in precisely delineating the complex architecture of the kidney has been well-established. Volumetric segmentation technologies, such as deformable registration, have shown promise in addressing the issue of variability in renal imaging. This variability can arise from differences across subjects as well as within the same subject. By offering an accurate template for image segmentation, these technologies have the potential to mitigate this obstacle effectively. The integration of deep learning frameworks and volumetric segmentation algorithms offers a robust and effective solution for the automated segmentation of kidneys. These methods can improve the accuracy of diagnosing kidney-related illnesses, specifically renal cysts, and offer the potential to better the monitoring of clinical development in individuals with such conditions.