Title: Skull part relationships and shape prediction toward the missing part completion

Authors: Tan-Nhu Nguyen; Ngoc-Bich Le; Xuan-Hien Quach-Nguyen; Thi-Hiep Nguyen; Van-Toi Vo; Tien-Tuan Dao

Addresses: School of Biomedical Engineering, International University, Zone 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' School of Biomedical Engineering, International University, Zone 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' School of Biomedical Engineering, International University, Zone 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' School of Biomedical Engineering, International University, Zone 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' School of Biomedical Engineering, International University, Zone 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' Univ. Lille, CNRS, Centrale Lille, UMR 9013-LaMcube-Laboratoire de Mécanique, Multiphysique, Multiéchelle, Lille, F-59000, France

Abstract: Accurate cranial reconstruction needs clear relation among skull parts due to the asymmetry of the skull structures. Consequently, this study investigated the relation among skull parts for enhancing the skull missing part prediction. The relationship was trained from three-dimensional skull shapes reconstructed from 329 head-and-neck computed tomography images. We automatically defined the skull parts throughout all skull shapes. The skull parts were parameterised using the principal component analysis (PCA). Skull part relations were trained through their PCA-based shape parameters. The output skull parts could be predicted from the input skull parts with the trained shape relation with good and acceptable accuracy in cranial reconstruction. The best and worst mean errors were 1.32 mm and 2.54 mm when the number of missing skull parts was one and ten, respectively. The investigated procedure was employed in a computer-aided system for automatically predicting and printing skull missing parts directly in 3D spaces.

Keywords: skull part relationship; skull shape prediction; statistical shape modelling; skull part fixing; cranial reconstruction.

DOI: 10.1504/IJBET.2024.143286

International Journal of Biomedical Engineering and Technology, 2024 Vol.46 No.4, pp.299 - 322

Received: 20 Feb 2024
Accepted: 01 May 2024
Published online: 12 Dec 2024 *

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