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 proceedingConference contribution

3 Citations (Scopus)

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 languageEnglish
Title of host publicationMedical Computer Vision
Subtitle of host publicationAlgorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
EditorsMichael Kelm, Henning Müller, Bjoern Menze, Shaoting Zhang, Dimitris Metaxas, Georg Langs, Albert Montillo, Weidong Cai
PublisherSpringer Verlag
Pages72-82
Number of pages11
ISBN (Print)9783319420158
DOIs
Publication statusPublished - 2016 Jan 1
EventInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany
Duration: 2015 Oct 92015 Oct 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9601 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
CountryGermany
CityGermany
Period15-10-0915-10-09

Fingerprint

Random Forest
Classifiers
Segmentation
Classifier
Majority Voting
Soft Tissue
Hardening
Image segmentation
Inhomogeneity
Image Segmentation
Tissue
Experimental Results
Estimate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wang, L., Gao, Y., Shi, F., Li, G., Chen, K-C., Tang, Z., ... 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
Wang, Li ; Gao, Yaozong ; Shi, Feng ; Li, Gang ; Chen, Ken-Chung ; Tang, Zhen ; Xia, James J. ; Shen, Dinggang. / 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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Wang, L, Gao, Y, Shi, F, Li, G, Chen, K-C, 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, 15-10-09. 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; Li, Gang; Chen, Ken-Chung; Tang, Zhen; Xia, James J.; Shen, Dinggang.

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 proceedingConference contribution

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AU - Wang, Li

AU - Gao, Yaozong

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AU - Li, Gang

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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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M3 - Conference contribution

SN - 9783319420158

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Cai, Weidong

PB - Springer Verlag

ER -

Wang L, Gao Y, Shi F, Li G, Chen K-C, 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)). https://doi.org/10.1007/978-3-319-42016-5_7