Automatic lung lobe segmentation using particles, thin plate splines, and maximum a posteriori estimation

Authors:
James C. Ross;Raúl San José Estépar;Gordon Kindlmann;Alejandro Díaz;Carl-Fredrik Westin;Edwin K. Silverman;George R. Washko
Affiliations:
Channing Laboratory, Brigham and Women's Hospital, Boston, MA and Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA and Surgical Planning Lab, ...;Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA and Surgical Planning Lab, Brigham and Women's Hospital, Boston, MA;Computer Science Department and Computation Institute, University of Chicago, Chicago, IL;Pontificia Universidad Catolica de Chile, Chile and Pulmonary and Critical Care Division, Brigham and Women's Hospital, Boston, MA;Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA and Surgical Planning Lab, Brigham and Women's Hospital, Boston, MA;Channing Laboratory, Brigham and Women's Hospital, Boston, MA and Pulmonary and Critical Care Division, Brigham and Women's Hospital, Boston, MA;Pulmonary and Critical Care Division, Brigham and Women's Hospital, Boston, MA
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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Abstract

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.