Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting

Abstract

Purpose: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Figure 1

Overview of the method steps for fissure detection and lobe segmentation.

Figure 2

Mean right horizontal (subscript h) and right oblique (subscript o) boundaries for a typical case (center rendering) together with changes in the first mode of variation.

Figure 3

Rendering of fissure particles resulting from each step of the proposed method. Particles are rendered with plate-like glyphs, and lungs are rendered semitransparently for context (sagittal view). From left to right: ridge particles sampling result and initial connected components (Subsection 2A), particles data after classification (Subsection 2D), and particles before lobe labeling (Subsection 2E). (Top) left lung; (bottom) right lung.

Figure 4

Amount of emphysema as measured by the fraction of the lung region falling below the −950 HU threshold for the cases used in this study. Reported amounts for the expiratory cases were taken from the cases' corresponding inspiratory scans.

Figure 5

Sagittal, axial, and coronal views illustrating segmentation results for two INSP test set cases. Note the severe emphysema and the resulting fissure distortion in the topmost case.

Figure 6

Sagittal, axial, and coronal views illustrating segmentation results for two EXP test set cases.

Figure 7

Dice scores for the left upper lobe (LUL), left lower lobe (LLL), right upper lobe (RUL), right middle lobe (RML), and right lower lobe (RLL) reported for the expiratory (top) and inspiratory (bottom) datasets. (No Dice scores equaling 1.0 were observed).

Figure 8

Left lung failure case (left: sagittal CT slice, middle: automatic segmentation result, right: reference standard). The severe lower lobe emphysema in combination with the relatively unusual fissure location caused the model fitting stage to fail. Therefore, subsequent classification, filtering, and segmentation stages failed as well.

Figure 9

Case for which the right horizontal fissure model was initialized too far from the true fissure location and became heavily distorted as it latched on to nonhorizontal fissure particles. The mean right horizontal fissure boundary was therefore used, but this still results in an unsatisfactory segmentation. Solid arrows indicate the true fissure location. Open arrows indicate a region of the right oblique that curls downward and was not properly captured with the automatic segmentation.

Figure 10

A difficult expiratory case (left: sagittal CT slice, middle: automatic segmentation result, right: reference standard). The right horizontal fissure is mostly incomplete, and the particles sampling were not able to adequately capture the right oblique fissure. The final automatic segmentation reflects good lobe boundary detection near well-sampled areas (arrows, left), but the reference standard segmentation indicates poorly captured regions (arrows, right).

Figure 11

(Top) Left and right lung particles sampling result for one of the training set cases (sagittal view). Particles are rendered with plate-like glyphs, and lungs are rendered semitransparently for context. (Bottom) corresponding ground truth particles.

Figure 12

ROC analysis of prefiltering parameter settings. Four θ_thresh values are considered (70°, 75°, 80°, and 85°) across a wide range of n_thresh values.

Figure 13

Convergence rate of model fitting to a typical fissure for various numbers of variation modes.

Figure 14

Left: Scatter plot of the 2D features space. The grouping in the lower left represents true fissure particles; the grouping towards the middle represents noise particles. (Middle) histograms of projected noise particles (left-most distribution), and fissure particles (right-most distribution). The x axis is unitless. (Right) corresponding ROC curve representing the distributions of the projected data.