Atlas-based estimation of lung and lobar anatomy in proton MRI

Nicholas J. Tustison, Kun Qing, Chengbo Wang, Talissa A. Altes, John P. Mugler

Research output: Journal PublicationArticlepeer-review

18 Citations (Scopus)

Abstract

Purpose: 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. Methods: 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. Results: 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). Conclusion: 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.

Original languageEnglish
Pages (from-to)315-320
Number of pages6
JournalMagnetic Resonance in Medicine
Volume76
Issue number1
DOIs
Publication statusPublished - 1 Jul 2016
Externally publishedYes

Keywords

  • advanced normalization tools
  • lobe segmentation
  • lung segmentation
  • multi-atlas label fusion
  • pulmonary image registration

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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