title: AISLE: an automatic volumetric segmentation method for the study of lung allometry.
abstract: We developed a fully automatic segmentation method for volumetric CT (computer tomography) datasets to support construction of a statistical atlas for the study of allometric laws of the lung. The proposed segmentation method, AISLE (Automated ITK-Snap based on Level-set), is based on the level-set implementation from an existing semi-automatic segmentation program, ITK-Snap. AISLE can segment the lung field without human interaction and provide intermediate graphical results as desired. The preliminary experimental results show that the proposed method can achieve accurate segmentation, in terms of volumetric overlap metric, by comparing with the ground-truth segmentation performed by a radiologist.
authors.affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. hlren@jhu.edu
authors.forename: Hongliang
authors.initials: H
authors.lastname: Ren
authors.forename: Peter
authors.initials: P
authors.lastname: Kazanzides
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000033:anatomy & histology / Q000000981:diagnostic imaging* / Q000254:growth & development*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*