Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable.
The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes.
The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT.
The proposed algorithm is effective for lung lobe segmentation in absence of auxiliary structures such as vessels and airways. The most challenging cases are those with mostly incomplete, absent, or near-absent fissures and in cases with poorly revealed fissures due to high image noise. However, the authors observe good performance even in the majority of these cases.
This work was partially funded by 1R01HL116931-01 and the COPDGene study NHLBI grants 2R01HL089897-06A1 and 2R01HL089856-06A1. Additional support provided by NIH grants K25 HL104085-04, K23HL089353-05, 1P50HL107192, R01HL116473, and R01HL107246.
II.A. Ridge surface sampling
II.B. Lobe boundary model construction
II.B.2. Case-specific fissure model construction
II.C. Lobe boundary model fitting
II.C.1. Determining particle to TPS surface distance: Newton's method
II.C.2. Fitting TPS surface model to particles data: Nelder-Mead simplex-reflection method
II.D. Classification using Fisher's linear discriminant
II.E. Lobe labeling
III. MATERIALS AND EXPERIMENTAL DESIGN
III.A. Testing data
III.B. Training datasets
V. DISCUSSION AND CONCLUSION
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