title: Atlas-based estimation of lung and lobar anatomy in proton MRI.
abstract: To propose an accurate methodological framework for automatically segmenting pulmonary proton MRI based on an optimal consensus of a spatially normalized library of annotated lung atlases.
  
  
  
  A library of 62 manually annotated lung atlases comprising 48 mixed healthy, chronic obstructive pulmonary disease, and asthmatic subjects of a large age range with multiple ventilation levels is used to produce an optimal segmentation in proton MRI, based on a consensus of the spatially normalized library. An extension of this methodology is used to provide best-guess estimates of lobar subdivisions in proton MRI from annotated computed tomography data.
  
  
  
  A leave-one-out evaluation strategy was used for evaluation. Jaccard overlap measures for the left and right lungs were used for performance comparisons relative to the current state-of-the-art (0.966 ± 0.018 and 0.970 ± 0.016, respectively). Best-guess estimates for the lobes exhibited comparable performance levels (left upper: 0.882 ± 0.059, left lower: 0.868 ± 0.06, right upper: 0.852 ± 0.067, right middle: 0.657 ± 0.130, right lower: 0.873 ± 0.063).
  
  
  
  An annotated atlas library approach can be used to provide good lung and lobe estimation in proton MRI. The proposed framework is useful for subsequent anatomically based analysis of structural and/or functional pulmonary image data. Magn Reson Med 76:315-320, 2016. © 2015 Wiley Periodicals, Inc.
authors.affiliation: Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Tustison
authors.affiliation: Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
authors.forename: Kun
authors.initials: K
authors.lastname: Qing
authors.affiliation: Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
authors.forename: Chengbo
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
authors.forename: Talissa A
authors.initials: TA
authors.lastname: Altes
authors.affiliation: Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
authors.forename: John P
authors.initials: JP
authors.lastname: Mugler
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D066244:Proton Magnetic Resonance Spectroscopy / Q000379:methods; D029424:Pulmonary Disease, Chronic Obstructive / Q000000981:diagnostic imaging* / Q000473:pathology; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: advanced normalization tools; lobe segmentation; lung segmentation; multi-atlas label fusion; pulmonary image registration