Comprehensive 3D Analysis of the Renal System and Stones: Segmenting and Registering Non-Contrast and Contrast Computed Tomography Images

Abstract

Detecting and accurately locating kidney stones, which are common urological conditions, can be challenging when using imaging examinations. Therefore, the primary objective of this research is to develop an ensemble model that integrates segmentation and registration techniques. This model aims to visualize the inner structure of the kidney and accurately identify any underlying kidney stones. To achieve this, three separate datasets, namely non-contrast computed tomography (CT) scans, corticomedullary CT scans, and CT excretory scans, are annotated to enhance the three-dimensional (3D) reconstruction of the kidney’s complex anatomy. Initially, the research focuses on utilizing segmentation models to identify and annotate specific classes within the annotated datasets. Subsequently, a registration algorithm is employed to align and combine the segmented results, resulting in a comprehensive 3D representation of the kidney’s anatomical structure. Three cutting-edge segmentation algorithms are employed and evaluated during the segmentation phase, with the most accurate segments being selected for the subsequent registration process. Ultimately, the registration process successfully aligns the kidneys across all three phases and combines the segmented labels, producing a detailed 3D visualization of the complete kidney structure. For kidney segmentation, Swin UNETR exhibited the highest Dice score of 95.21%; for stone segmentation, ResU-Net achieved the highest Dice score of 87.69%. Regarding Artery, Cortex, and Medulla segmentation, ResU-Net and 3D U-Net show comparable performance with similar Dice scores. Considering the Collecting System and Parenchyma, ResU-Net and 3D U-Net demonstrate similar performance in Dice scores. In conclusion, the proposed ensemble model shows potential in accurately visualizing the internal structure of the kidney and precisely localizing kidney stones. This advancement improves the diagnosis process and preoperative planning in percutaneous nephrolithotomy.