An Unsupervised Computed Tomography Kidney Segmentation with Multi-Region Clustering and Adaptive Active Contours

Abstract

Kidney segmentation from abdominal computed tomography (CT) images is essential for computer-aided kidney diagnosis, pathology detection, and surgical planning. This paper introduces a kidney segmentation method for clinical contrast-enhanced CT images. First, it begins with shape-based preprocessing to remove the spine and ribs. Second, a novel clustering algorithm and an initial kidney selection strategy are utilized to locate the initial slices and contours. Finally, an adaptive narrow-band approach based on active contours is developed, followed by a clustering postprocessing to address issues with concave parts. Experimental results demonstrate the high segmentation performance of the proposed method, achieving a Dice Similarity Coefficient of 97.4 ± 1.0% and an Average Symmetric Surface Distance of 0.5 ± 0.2 mm across twenty sequences. Notably, this method eliminates the need for manually setting initial contours and can handle intensity inhomogeneity and varying kidney shapes without extensive training or statistical modeling.