title: Review of automatic pulmonary lobe segmentation methods from CT.
abstract: The computational detection of pulmonary lobes from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. Several authors have proposed automated algorithms and we present a methodological review. These algorithms share a number of common stages and we consider each stage in turn, comparing the methods applied by each author and discussing their relative strengths. No standard method has yet emerged and none of the published methods have been demonstrated across a full range of clinical pathologies and imaging protocols. We discuss how improved methods could be developed by combining different approaches, and we use this to propose a workflow for the development of new algorithms.
authors.affiliation: Department of Computer Science, University of Oxford, Oxford, UK. Electronic address: tom.doel@cs.ox.ac.uk.
authors.forename: Tom
authors.initials: T
authors.lastname: Doel
authors.affiliation: Department of Computer Science, University of Oxford, Oxford, UK.
authors.forename: David J
authors.initials: DJ
authors.lastname: Gavaghan
authors.affiliation: Department of Engineering Science and Oxford e-Research Centre, University of Oxford, Oxford, UK.
authors.forename: Vicente
authors.initials: V
authors.lastname: Grau
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008168:Lung / 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; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: CT; Fissure; Lobe; Lung; Pulmonary; Segmentation