We present a fully automatic lung lobe segmentation algorithm that is effective in high resolution computed tomography (CT) datasets in the presence of confounding factors such as incomplete fissures (anatomical structures indicating lobe boundaries), advanced disease states, high body mass index (BMI), and low-dose scanning protocols. In contrast to other algorithms that leverage segmentations of auxiliary structures (esp. vessels and airways), we rely only upon image features indicating fissure locations. We employ a particle system that samples the image domain and provides a set of candidate fissure locations. We follow this stage with maximum a posteriori (MAP) estimation to eliminate poor candidates and then perform a post-processing operation to remove remaining noise particles. We then fit a thin plate spline (TPS) interpolating surface to the fissure particles to form the final lung lobe segmentation. Results indicate that our algorithm performs comparably to pulmonologist-generated lung lobe segmentations on a set of challenging cases.
James C.Ross Raul San Jose Estepar Gordon Kindlmann Alejandro Diaz Carl-FredrikWestin Edwin K.Silverman George R.Washko
Channing Laboratory, Brigham and Women’s Hospital, Boston, MA Laboratory of Mathematics in Imaging, Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital,Harvard Medical School, Boston, M Computer Science Department and Computation Institute, University of Chicago, Chicago, IL Pontificia Universidad Catolica de Chile, Chile6 Pulmonary and Critical Care Division, Brigham and W Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital,Harvard Medical School, Boston, M Channing Laboratory, Brigham and Women’s Hospital, Boston, MA Pulmonary and Critical Care Division, Pulmonary and Critical Care Division, Brigham and Women’s Hospital, Boston, MA