Lung Nodule Growth Analysis from 3D CT Data Using a Coupled Segmentation and Registration Framework

Abstract

In this paper we propose a new framework to simultaneously segment and register lung and tumor in serial CT data. Our method assumes nonrigid transformation on lung deformation and rigid structure on the tumor. We use the BSpline- based nonrigid transformation to model the lung deformation while imposing rigid transformation on the tumor to preserve the volume and the shape of the tumor. In particular, we set the control points within the tumor to form a control mesh and thus assume the tumor region follows the same rigid transformation as the control mesh. For segmentation, we apply a 2D graph-cut algorithm on the 3D lung and tumor datasets. By iteratively performing segmentation and registration, our method achieves highly accurate segmentation and registration on serial CT data. Finally, since our method eliminates the possible volume variations of the tumor during registration, we can further estimate accurately the tumor growth, an important evidence in lung cancer diagnosis. Initial experiments on five sets of patients’ serial CT data show that our method is robust and reliable.