Automated segmentation of CBCT image using spiral CT atlases and convex optimization

Li Wang, Ken-Chung Chen, Feng Shi, Shu Liao, Gang Li, Yaozong Gao, Steve G.F. Shen, Jin Yan, Philip K.M. Lee, Ben Chow, Nancy X. Liu, James J. Xia, Dinggang Shen

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

18 Citations (Scopus)

Abstract

Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages251-258
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 2013 Oct 24
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 2013 Sep 222013 Sep 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8151 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period13-09-2213-09-26

Fingerprint

Convex optimization
Atlas
Computed Tomography
Convex Optimization
Tomography
Cones
Cone
Segmentation
Computerized tomography
Image segmentation
Maximum a Posteriori
Soft Tissue
Dosimetry
Hardening
Labels
Fusion reactions
Inhomogeneity
Image Segmentation
Modality
Tissue

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wang, L., Chen, K-C., Shi, F., Liao, S., Li, G., Gao, Y., ... Shen, D. (2013). Automated segmentation of CBCT image using spiral CT atlases and convex optimization. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 3 ed., pp. 251-258). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-40760-4_32
Wang, Li ; Chen, Ken-Chung ; Shi, Feng ; Liao, Shu ; Li, Gang ; Gao, Yaozong ; Shen, Steve G.F. ; Yan, Jin ; Lee, Philip K.M. ; Chow, Ben ; Liu, Nancy X. ; Xia, James J. ; Shen, Dinggang. / Automated segmentation of CBCT image using spiral CT atlases and convex optimization. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3. ed. 2013. pp. 251-258 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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abstract = "Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations.",
author = "Li Wang and Ken-Chung Chen and Feng Shi and Shu Liao and Gang Li and Yaozong Gao and Shen, {Steve G.F.} and Jin Yan and Lee, {Philip K.M.} and Ben Chow and Liu, {Nancy X.} and Xia, {James J.} and Dinggang Shen",
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Wang, L, Chen, K-C, Shi, F, Liao, S, Li, G, Gao, Y, Shen, SGF, Yan, J, Lee, PKM, Chow, B, Liu, NX, Xia, JJ & Shen, D 2013, Automated segmentation of CBCT image using spiral CT atlases and convex optimization. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8151 LNCS, pp. 251-258, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 13-09-22. https://doi.org/10.1007/978-3-642-40760-4_32

Automated segmentation of CBCT image using spiral CT atlases and convex optimization. / Wang, Li; Chen, Ken-Chung; Shi, Feng; Liao, Shu; Li, Gang; Gao, Yaozong; Shen, Steve G.F.; Yan, Jin; Lee, Philip K.M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang.

Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3. ed. 2013. p. 251-258 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3).

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

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AU - Li, Gang

AU - Gao, Yaozong

AU - Shen, Steve G.F.

AU - Yan, Jin

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AU - Shen, Dinggang

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AB - Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations.

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M3 - Conference contribution

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BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings

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Wang L, Chen K-C, Shi F, Liao S, Li G, Gao Y et al. Automated segmentation of CBCT image using spiral CT atlases and convex optimization. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3 ed. 2013. p. 251-258. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-40760-4_32