Automated segmentation of CBCT image with prior-guided sequential random forest

Li Wang; Yaozong Gao; Feng Shi; Gang Li; Ken Chung Chen; Zhen Tang; James J. Xia; Dinggang Shen

doi:10.1007/978-3-319-42016-5_7

Automated segmentation of CBCT image with prior-guided sequential random forest

Li Wang, Yaozong Gao, Feng Shi, Gang Li, Ken Chung Chen, Zhen Tang, James J. Xia, Dinggang Shen

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

5 Scopus citations

Abstract

A major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulty for accurate segmentation of bony structures from soft tissues, as well as separation of mandible from maxilla. In this paper, we present a novel fully automated method for CBCT image segmentation. Specifically, we first employ majority voting to estimate the initial probability maps of mandible and maxilla. We then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of classifier. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of classifier. By iteratively training the subsequent classifier and the updated segmentation probability maps, we can derive a sequence of classifiers. Experimental results on 30 CBCTs show that the proposed method achieves the state-of-the-art performance.

Original language	English (US)
Title of host publication	Medical Computer Vision
Subtitle of host publication	Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
Editors	Michael Kelm, Henning Müller, Bjoern Menze, Shaoting Zhang, Dimitris Metaxas, Georg Langs, Albert Montillo, Weidong Cai
Publisher	Springer-Verlag
Pages	72-82
Number of pages	11
ISBN (Print)	9783319420158
DOIs	https://doi.org/10.1007/978-3-319-42016-5_7
State	Published - 2016
Event	International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany Duration: Oct 9 2015 → Oct 9 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9601 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Country/Territory	Germany
City	Germany
Period	10/9/15 → 10/9/15

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-42016-5_7

Cite this

Wang, L., Gao, Y., Shi, F., Li, G., Chen, K. C., Tang, Z., Xia, J. J., & Shen, D. (2016). Automated segmentation of CBCT image with prior-guided sequential random forest. In M. Kelm, H. Müller, B. Menze, S. Zhang, D. Metaxas, G. Langs, A. Montillo, & W. Cai (Eds.), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers (pp. 72-82). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-42016-5_7

Automated segmentation of CBCT image with prior-guided sequential random forest. / Wang, Li; Gao, Yaozong; Shi, Feng et al.
Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. ed. / Michael Kelm; Henning Müller; Bjoern Menze; Shaoting Zhang; Dimitris Metaxas; Georg Langs; Albert Montillo; Weidong Cai. Springer-Verlag, 2016. p. 72-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601 LNCS).

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

Wang, L, Gao, Y, Shi, F, Li, G, Chen, KC, Tang, Z, Xia, JJ & Shen, D 2016, Automated segmentation of CBCT image with prior-guided sequential random forest. in M Kelm, H Müller, B Menze, S Zhang, D Metaxas, G Langs, A Montillo & W Cai (eds), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9601 LNCS, Springer-Verlag, pp. 72-82, International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI, Germany, Germany, 10/9/15. https://doi.org/10.1007/978-3-319-42016-5_7

Wang L, Gao Y, Shi F, Li G, Chen KC, Tang Z et al. Automated segmentation of CBCT image with prior-guided sequential random forest. In Kelm M, Müller H, Menze B, Zhang S, Metaxas D, Langs G, Montillo A, Cai W, editors, Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Springer-Verlag. 2016. p. 72-82. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-42016-5_7

Wang, Li ; Gao, Yaozong ; Shi, Feng et al. / Automated segmentation of CBCT image with prior-guided sequential random forest. Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. editor / Michael Kelm ; Henning Müller ; Bjoern Menze ; Shaoting Zhang ; Dimitris Metaxas ; Georg Langs ; Albert Montillo ; Weidong Cai. Springer-Verlag, 2016. pp. 72-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulty for accurate segmentation of bony structures from soft tissues, as well as separation of mandible from maxilla. In this paper, we present a novel fully automated method for CBCT image segmentation. Specifically, we first employ majority voting to estimate the initial probability maps of mandible and maxilla. We then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of classifier. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of classifier. By iteratively training the subsequent classifier and the updated segmentation probability maps, we can derive a sequence of classifiers. Experimental results on 30 CBCTs show that the proposed method achieves the state-of-the-art performance.",

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N2 - A major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulty for accurate segmentation of bony structures from soft tissues, as well as separation of mandible from maxilla. In this paper, we present a novel fully automated method for CBCT image segmentation. Specifically, we first employ majority voting to estimate the initial probability maps of mandible and maxilla. We then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of classifier. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of classifier. By iteratively training the subsequent classifier and the updated segmentation probability maps, we can derive a sequence of classifiers. Experimental results on 30 CBCTs show that the proposed method achieves the state-of-the-art performance.

AB - A major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulty for accurate segmentation of bony structures from soft tissues, as well as separation of mandible from maxilla. In this paper, we present a novel fully automated method for CBCT image segmentation. Specifically, we first employ majority voting to estimate the initial probability maps of mandible and maxilla. We then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of classifier. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of classifier. By iteratively training the subsequent classifier and the updated segmentation probability maps, we can derive a sequence of classifiers. Experimental results on 30 CBCTs show that the proposed method achieves the state-of-the-art performance.

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Automated segmentation of CBCT image with prior-guided sequential random forest

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