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Title: Automated Planning with Multivariate Shape Descriptors for Fibular Transfer in Mandibular Reconstruction
Authors: Nakao, Megumi  kyouindb  KAKEN_id  orcid (unconfirmed)
Aso, Shimpei
Imai, Yuichiro
Ueda, Nobuhiro
Hatanaka, Toshihide
Shiba, Mao
Kirita, Tadaaki
Matsuda, Tetsuya  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 中尾, 恵
Keywords: Biomedical Engineering
Issue Date: Aug-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Journal title: IEEE Transactions on Biomedical Engineering
Volume: 64
Issue: 8
Start page: 1772
End page: 1785
Abstract: Objective: This paper introduces methods to automate preoperative planning of fibular segmentation and placement for mandibular reconstruction with fibular flaps. Methods: Preoperative virtual planning for this type of surgery has been performed by manual adjustment of many parameters, or based upon a single feature of the reconstruction. We propose a novel planning procedure formulated as a non-convex minimization problem of an objective function using the multilateral shape descriptors. Results: A retrospective study was designed and 120 reconstruction plans were reproduced using computed tomography images with oral surgeons. The proposed automated planning model was quantitatively compared with both the existing model and the surgeons’ plans. Conclusion: The results show that the developed framework attains stable automated planning that agrees with the surgeons’ decisions. Significance: This method addresses trade-off problems between symmetric reconstruction and restoration of the native contour of the mandible.
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
This is not the published version. Please cite only the published version.
DOI(Published Version): 10.1109/TBME.2016.2621742
Appears in Collections:Journal Articles

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