Automated Kidney Segmentation for Traumatic Injured Patients through Ensemble Learning and Active Contour Modeling

Abstract

Traumatic abdominal injury can lead to multiple complications including laceration of major organs such as kidneys. Contrast-enhanced Computed Tomography (CT) is the primary imaging modality for evaluating kidney injury. However, the traditional visual examination of CT scans is time consuming, non-quantitative, prone to human error, and costly. In this work we propose a kidney segmentation method using machine learning and active contour modeling. We first detect an initialization mask inside the kidney and then evolve its boundary. This model is specifically developed and evaluated on trauma cases. Our experimental results show the average recall score of 92.6% and average Dice similarity value of 88.9%.