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Title: Kernel-based modeling of pneumothorax deformation using intraoperative cone-beam CT images
Authors: Nakao, Megumi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-5508-4366 (unconfirmed)
Maekawa, Hinako
Mineura, Katsutaka
Chen-Yoshikawa, Toyofumi F.
Matsuda, Tetsuya
Author's alias: 中尾, 恵
前川, 日南子
峯浦, 一貴
芳川, 豊史
松田, 哲也
Issue Date: 15-Feb-2021
Publisher: SPIE
Journal title: Proceedings; Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling
Volume: 11598
Thesis number: 115980P
Abstract: In this study, we introduce statistical modeling methods for pneumothorax deformation using paired cone-beam computed tomography (CT) images. We designed a deformable mesh registration framework for shape changes involving non-linear deformation and rotation of the lungs. The registered meshes with local correspondences are available for both surgical guidance in thoracoscopic surgery and building statistical deformation models with inter-patient variations. In addition, a kernel-based deformation learning framework is proposed to reconstruct intraoperative dfl ated states of the lung from the preoperative CT models. This paper reports the findings of pneumothorax deformation and evaluation results of the kernel-based deformation framework.
Description: Event: SPIE Medical Imaging, 2021, Online Only
Rights: © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
URI: http://hdl.handle.net/2433/264686
DOI(Published Version): 10.1117/12.2581388
Appears in Collections:Journal Articles

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