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Published in: International Journal of Computer Assisted Radiology and Surgery 10/2019

Open Access 01-10-2019 | Computed Tomography | Original Article

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

Authors: Megumi Nakao, Junko Tokuno, Toyofumi Chen-Yoshikawa, Hiroshi Date, Tetsuya Matsuda

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 10/2019

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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\(\,\hbox {H}_2\hbox {O}\)). 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.
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Metadata
Title
Surface deformation analysis of collapsed lungs using model-based shape matching
Authors
Megumi Nakao
Junko Tokuno
Toyofumi Chen-Yoshikawa
Hiroshi Date
Tetsuya Matsuda
Publication date
01-10-2019
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 10/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-019-02013-0

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