A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images

Abstract

A framework for 3D kidney segmentation from abdominal computed tomography (CT) images is proposed. Accurate kidney segmentation from CT images is a challenging task due to the large inhomogeneity of the kidney (e.g., cortex and medulla), inter-patient anatomical differences, etc. To account for these challenges, a novel framework utilizing random forest (RF) classification that has the ability to cluster complex data is proposed. To build a robust classification model, discriminative features are needed for better separation of data classes. In this work, regional features from the CT appearance, a kidney shape prior model, and higher-order spatial interactions are extracted and are used for tissue classification. The shape model is constructed using a set of training images and is updated during segmentation using an appearance-based method taking into account both voxels' locations and appearances. The spatial interactions between CT data voxels are modeled using a higher-order spatial model that adds to the pairwise cliques the families of the triple- and quad cliques. The proposed framework has been tested on CT data that has been collected from 20 subjects and consist of multiple 3D CT scans acquired at the pre-and post-contrast agent administration. Evaluation results, using both volumetric and distance-based metrics, between manually drawn and automatically segmented contours confirm the high accuracy of the proposed technique.