Dataset Information

Surface deformation analysis of collapsed lungs using model-based shape matching.

ABSTRACT: PURPOSE:To facilitate intraoperative localization of lung nodules, this study used model-based shape matching techniques to analyze the inter-subject three-dimensional surface deformation induced by pneumothorax. METHODS:Contrast- enhanced computed tomography (CT) images of the left lungs of 11 live beagle dogs were acquired at two bronchial pressures (14 and 2 cm[Formula: see text]). To address shape matching problems for largely deformed lung images with pixel intensity shift, a complete Laplacian-based shape matching solution that optimizes the differential displacement field was introduced. RESULTS:Experiments were performed to confirm the methods' registration accuracy using CT images of lungs. Shape similarity and target displacement errors in the registered models were improved compared with those from existing shape matching methods. Spatial displacement of the whole lung's surface was visualized with an average error of within 5 mm. CONCLUSION:The proposed methods address problems with the matching of surfaces with large curvatures and deformations and achieved smaller registration errors than existing shape matching methods, even at the tip and ridge regions. The findings and inter-subject statistical representation are directly available for further research on pneumothorax deformation modeling.

SUBMITTER: Nakao M

PROVIDER: S-EPMC6797649 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Surface deformation analysis of collapsed lungs using model-based shape matching.

Nakao Megumi M Tokuno Junko J Chen-Yoshikawa Toyofumi T Date Hiroshi H Matsuda Tetsuya T

International journal of computer assisted radiology and surgery 20190627 10

<h4>Purpose</h4>To facilitate intraoperative localization of lung nodules, this study used model-based shape matching techniques to analyze the inter-subject three-dimensional surface deformation induced by pneumothorax.<h4>Methods</h4>Contrast- enhanced computed tomography (CT) images of the left lungs of 11 live beagle dogs were acquired at two bronchial pressures (14 and 2 cm[Formula: see text]). To address shape matching problems for largely deformed lung images with pixel intensity shift, a ...[more]

PMID: 31250255

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Project description: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.

Dataset Information

Surface deformation analysis of collapsed lungs using model-based shape matching.

Publications

Surface deformation analysis of collapsed lungs using model-based shape matching.

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