Atlas‐based estimation of lung and lobar anatomy in proton MRI. Issue 1 (29th July 2015)

• Record Type: Journal Article
• Authors: Tustison, Nicholas J.; Qing, Kun; Wang, Chengbo; Altes, Talissa A.; Mugler, John P.
• Is Part Of: Magnetic resonance in medicine. Volume 76:Issue 1(2016)
• Page Start: 315
• Page End: 320
• Publication Date: 2015-07-29
• Subjects: advanced normalization tools -- lobe segmentation -- lung segmentation -- multi‐atlas label fusion -- pulmonary image registration; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.25824
• Languages: English
• ISSNs: 0740-3194

Abstract

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. © 2015 Wiley Periodicals, Inc.