Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks

Jun Zhang, Mingxia Liu, Li Wang, Si Chen, Peng Yuan, Jianfu Li, Steve Guo Fang Shen, Zhen Tang, Ken-Chung Chen, James J. Xia, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Generating accurate 3D models from cone-beam computed tomography (CBCT) images is an important step in developing treatment plans for patients with craniomaxillofacial (CMF) deformities. This process often involves bone segmentation and landmark digitization. Since anatomical landmarks generally lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly correlated. However, most existing methods simply treat them as two standalone tasks, without considering their inherent association. In addition, these methods usually ignore the spatial context information (i.e., displacements from voxels to landmarks) in CBCT images. To this end, we propose a context-guided fully convolutional network (FCN) for joint bone segmentation and landmark digitization. Specifically, we first train an FCN to learn the displacement maps to capture the spatial context information in CBCT images. Using the learned displacement maps as guidance information, we further develop a multi-task FCN to jointly perform bone segmentation and landmark digitization. Our method has been evaluated on 107 subjects from two centers, and the experimental results show that our method is superior to the state-of-the-art methods in both bone segmentation and landmark digitization.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
EditorsPierre Jannin, Simon Duchesne, Maxime Descoteaux, Alfred Franz, D. Louis Collins, Lena Maier-Hein
PublisherSpringer Verlag
Pages720-728
Number of pages9
ISBN (Print)9783319661841
DOIs
Publication statusPublished - 2017 Jan 1
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sep 112017 Sep 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10434 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period17-09-1117-09-13

Fingerprint

Digitization
Analog to digital conversion
Landmarks
Bone
Segmentation
Computed Tomography
Tomography
Cones
Cone
Addition method
Voxel
Context
3D Model
Guidance
Experimental Results

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhang, J., Liu, M., Wang, L., Chen, S., Yuan, P., Li, J., ... Shen, D. (2017). Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. In P. Jannin, S. Duchesne, M. Descoteaux, A. Franz, D. L. Collins, & L. Maier-Hein (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (pp. 720-728). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10434 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66185-8_81
Zhang, Jun ; Liu, Mingxia ; Wang, Li ; Chen, Si ; Yuan, Peng ; Li, Jianfu ; Shen, Steve Guo Fang ; Tang, Zhen ; Chen, Ken-Chung ; Xia, James J. ; Shen, Dinggang. / Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. editor / Pierre Jannin ; Simon Duchesne ; Maxime Descoteaux ; Alfred Franz ; D. Louis Collins ; Lena Maier-Hein. Springer Verlag, 2017. pp. 720-728 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Generating accurate 3D models from cone-beam computed tomography (CBCT) images is an important step in developing treatment plans for patients with craniomaxillofacial (CMF) deformities. This process often involves bone segmentation and landmark digitization. Since anatomical landmarks generally lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly correlated. However, most existing methods simply treat them as two standalone tasks, without considering their inherent association. In addition, these methods usually ignore the spatial context information (i.e., displacements from voxels to landmarks) in CBCT images. To this end, we propose a context-guided fully convolutional network (FCN) for joint bone segmentation and landmark digitization. Specifically, we first train an FCN to learn the displacement maps to capture the spatial context information in CBCT images. Using the learned displacement maps as guidance information, we further develop a multi-task FCN to jointly perform bone segmentation and landmark digitization. Our method has been evaluated on 107 subjects from two centers, and the experimental results show that our method is superior to the state-of-the-art methods in both bone segmentation and landmark digitization.",
author = "Jun Zhang and Mingxia Liu and Li Wang and Si Chen and Peng Yuan and Jianfu Li and Shen, {Steve Guo Fang} and Zhen Tang and Ken-Chung Chen and Xia, {James J.} and Dinggang Shen",
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Zhang, J, Liu, M, Wang, L, Chen, S, Yuan, P, Li, J, Shen, SGF, Tang, Z, Chen, K-C, Xia, JJ & Shen, D 2017, Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. in P Jannin, S Duchesne, M Descoteaux, A Franz, DL Collins & L Maier-Hein (eds), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10434 LNCS, Springer Verlag, pp. 720-728, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17-09-11. https://doi.org/10.1007/978-3-319-66185-8_81

Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. / Zhang, Jun; Liu, Mingxia; Wang, Li; Chen, Si; Yuan, Peng; Li, Jianfu; Shen, Steve Guo Fang; Tang, Zhen; Chen, Ken-Chung; Xia, James J.; Shen, Dinggang.

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. ed. / Pierre Jannin; Simon Duchesne; Maxime Descoteaux; Alfred Franz; D. Louis Collins; Lena Maier-Hein. Springer Verlag, 2017. p. 720-728 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10434 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Liu, Mingxia

AU - Wang, Li

AU - Chen, Si

AU - Yuan, Peng

AU - Li, Jianfu

AU - Shen, Steve Guo Fang

AU - Tang, Zhen

AU - Chen, Ken-Chung

AU - Xia, James J.

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AB - Generating accurate 3D models from cone-beam computed tomography (CBCT) images is an important step in developing treatment plans for patients with craniomaxillofacial (CMF) deformities. This process often involves bone segmentation and landmark digitization. Since anatomical landmarks generally lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly correlated. However, most existing methods simply treat them as two standalone tasks, without considering their inherent association. In addition, these methods usually ignore the spatial context information (i.e., displacements from voxels to landmarks) in CBCT images. To this end, we propose a context-guided fully convolutional network (FCN) for joint bone segmentation and landmark digitization. Specifically, we first train an FCN to learn the displacement maps to capture the spatial context information in CBCT images. Using the learned displacement maps as guidance information, we further develop a multi-task FCN to jointly perform bone segmentation and landmark digitization. Our method has been evaluated on 107 subjects from two centers, and the experimental results show that our method is superior to the state-of-the-art methods in both bone segmentation and landmark digitization.

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Zhang J, Liu M, Wang L, Chen S, Yuan P, Li J et al. Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. In Jannin P, Duchesne S, Descoteaux M, Franz A, Collins DL, Maier-Hein L, editors, Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Springer Verlag. 2017. p. 720-728. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-66185-8_81