Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects

Deqiang Xiao; Li Wang; Hannah Deng; Kim Han Thung; Jihua Zhu; Peng Yuan; Yriu L. Rodrigues; Leonel Perez; Christopher E. Crecelius; Jaime Gateno; Tiansku Kuang; Steve G.F. Shen; Daeseung Kim; David M. Alfi; Pew Thian Yap; James J. Xia; Dinggang Shen

doi:10.1007/978-3-030-32254-0_37

Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects

Deqiang Xiao
, Li Wang
, Hannah Deng
, Kim Han Thung
, Jihua Zhu
, Peng Yuan
, Yriu L. Rodrigues
, Leonel Perez
, Christopher E. Crecelius
, Jaime Gateno
, Tiansku Kuang
, Steve G.F. Shen
, Daeseung Kim
, David M. Alfi
, Pew Thian Yap
, James J. Xia
, Dinggang Shen^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

In this paper, we introduce a method for estimating patient-specific reference bony shape models for planning of reconstructive surgery for patients with acquired craniomaxillofacial (CMF) trauma. We propose an automatic bony shape estimation framework using pre-traumatic portrait photographs and post-traumatic head computed tomography (CT) scans. A 3D facial surface is first reconstructed from the patient’s pre-traumatic photographs. An initial estimation of the patient’s normal bony shape is then obtained with the reconstructed facial surface via sparse representation using a dictionary of paired facial and bony surfaces of normal subjects. We further refine the bony shape model by deforming the initial bony shape model to the post-traumatic 3D CT bony model, regularized by a statistical shape model built from a database of normal subjects. Experimental results show that our method is capable of effectively recovering the patient’s normal facial bony shape in regions with defects, allowing CMF surgical planning to be performed precisely for a wider range of defects caused by trauma.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Editors	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	327-335
Number of pages	9
ISBN (Print)	9783030322533
DOIs	https://doi.org/10.1007/978-3-030-32254-0_37
Publication status	Published - 2019
Event	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 2019 Oct 13 → 2019 Oct 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11768 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	19/10/13 → 19/10/17

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Adaptive-focus deformable shape model (AFDSM)
Craniomaxillofacial (CMF)
Facial bone estimation
Simulation
Sparse representation
Surgical planning
Three-dimensional facial reconstruction
Trauma

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-32254-0_37

Cite this

Xiao, D., Wang, L., Deng, H., Thung, K. H., Zhu, J., Yuan, P., Rodrigues, Y. L., Perez, L., Crecelius, C. E., Gateno, J., Kuang, T., Shen, S. G. F., Kim, D., Alfi, D. M., Yap, P. T., Xia, J. J., & Shen, D. (2019). Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects. In D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 327-335). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11768 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32254-0_37

Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects. / Xiao, Deqiang; Wang, Li; Deng, Hannah et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. p. 327-335 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11768 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xiao, D, Wang, L, Deng, H, Thung, KH, Zhu, J, Yuan, P, Rodrigues, YL, Perez, L, Crecelius, CE, Gateno, J, Kuang, T, Shen, SGF, Kim, D, Alfi, DM, Yap, PT, Xia, JJ & Shen, D 2019, Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11768 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 327-335, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 19/10/13. https://doi.org/10.1007/978-3-030-32254-0_37

Xiao D, Wang L, Deng H, Thung KH, Zhu J, Yuan P et al. Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects. In Shen D, Yap PT, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2019. p. 327-335. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32254-0_37

Xiao, Deqiang ; Wang, Li ; Deng, Hannah et al. / Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. pp. 327-335 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we introduce a method for estimating patient-specific reference bony shape models for planning of reconstructive surgery for patients with acquired craniomaxillofacial (CMF) trauma. We propose an automatic bony shape estimation framework using pre-traumatic portrait photographs and post-traumatic head computed tomography (CT) scans. A 3D facial surface is first reconstructed from the patient{\textquoteright}s pre-traumatic photographs. An initial estimation of the patient{\textquoteright}s normal bony shape is then obtained with the reconstructed facial surface via sparse representation using a dictionary of paired facial and bony surfaces of normal subjects. We further refine the bony shape model by deforming the initial bony shape model to the post-traumatic 3D CT bony model, regularized by a statistical shape model built from a database of normal subjects. Experimental results show that our method is capable of effectively recovering the patient{\textquoteright}s normal facial bony shape in regions with defects, allowing CMF surgical planning to be performed precisely for a wider range of defects caused by trauma.",

keywords = "Adaptive-focus deformable shape model (AFDSM), Craniomaxillofacial (CMF), Facial bone estimation, Simulation, Sparse representation, Surgical planning, Three-dimensional facial reconstruction, Trauma",

author = "Deqiang Xiao and Li Wang and Hannah Deng and Thung, \{Kim Han\} and Jihua Zhu and Peng Yuan and Rodrigues, \{Yriu L.\} and Leonel Perez and Crecelius, \{Christopher E.\} and Jaime Gateno and Tiansku Kuang and Shen, \{Steve G.F.\} and Daeseung Kim and Alfi, \{David M.\} and Yap, \{Pew Thian\} and Xia, \{James J.\} and Dinggang Shen",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

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AU - Xiao, Deqiang

AU - Wang, Li

AU - Deng, Hannah

AU - Thung, Kim Han

AU - Zhu, Jihua

AU - Yuan, Peng

AU - Rodrigues, Yriu L.

AU - Perez, Leonel

AU - Crecelius, Christopher E.

AU - Gateno, Jaime

AU - Kuang, Tiansku

AU - Shen, Steve G.F.

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AU - Alfi, David M.

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AU - Xia, James J.

AU - Shen, Dinggang

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N2 - In this paper, we introduce a method for estimating patient-specific reference bony shape models for planning of reconstructive surgery for patients with acquired craniomaxillofacial (CMF) trauma. We propose an automatic bony shape estimation framework using pre-traumatic portrait photographs and post-traumatic head computed tomography (CT) scans. A 3D facial surface is first reconstructed from the patient’s pre-traumatic photographs. An initial estimation of the patient’s normal bony shape is then obtained with the reconstructed facial surface via sparse representation using a dictionary of paired facial and bony surfaces of normal subjects. We further refine the bony shape model by deforming the initial bony shape model to the post-traumatic 3D CT bony model, regularized by a statistical shape model built from a database of normal subjects. Experimental results show that our method is capable of effectively recovering the patient’s normal facial bony shape in regions with defects, allowing CMF surgical planning to be performed precisely for a wider range of defects caused by trauma.

AB - In this paper, we introduce a method for estimating patient-specific reference bony shape models for planning of reconstructive surgery for patients with acquired craniomaxillofacial (CMF) trauma. We propose an automatic bony shape estimation framework using pre-traumatic portrait photographs and post-traumatic head computed tomography (CT) scans. A 3D facial surface is first reconstructed from the patient’s pre-traumatic photographs. An initial estimation of the patient’s normal bony shape is then obtained with the reconstructed facial surface via sparse representation using a dictionary of paired facial and bony surfaces of normal subjects. We further refine the bony shape model by deforming the initial bony shape model to the post-traumatic 3D CT bony model, regularized by a statistical shape model built from a database of normal subjects. Experimental results show that our method is capable of effectively recovering the patient’s normal facial bony shape in regions with defects, allowing CMF surgical planning to be performed precisely for a wider range of defects caused by trauma.

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A2 - Zhou, Sean

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ER -

Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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