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

• Published in: International journal for computer assisted radiology and surgery Vol. 14; no. 10; pp. 1763 - 1774
• Main Authors: Nakao, Megumi, Tokuno, Junko, Chen-Yoshikawa, Toyofumi, Date, Hiroshi, Matsuda, Tetsuya
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2019; Springer Nature B.V
• Subjects: Algorithms; Animals; Computed tomography; Computer Imaging; Computer Science; Deformation; Deformation analysis; Disease Models, Animal; Displacement; Dogs; Health Informatics; Humans; Image acquisition; Image contrast; Image enhancement; Imaging; Lungs; Medical imaging; Medicine; Medicine & Public Health; Nodules; Original; Original Article; Pattern Recognition and Graphics; Pneumothorax; Pneumothorax - diagnostic imaging; Radiology; Surface matching; Surgery; Thoracoscopy - methods; Three dimensional analysis; Three dimensional models; Tomography, X-Ray Computed - methods; Video-Assisted Surgery - methods; Vision; Model-based shape matching; Pneumothorax deformation analysis; Lung; Thoracoscopic surgery
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-019-02013-0

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 H 2 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.