Automatic lung lobe segmentation using particles, thin plate splines, and maximum a posteriori estimation.
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.
Ross
James C
JC
Channing Laboratory, Brigham and Women's Hospital, Boston, MA, USA.
San José Estépar
Rail
R
Kindlmann
Gordon
G
Díaz
Alejandro
A
Westin
Carl-Fredrik
CF
Silverman
Edwin K
EK
Washko
George R
GR
D000465:Algorithms; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*