Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model

Jun Zhang, Yaozong Gao, Li Wang, Zhen Tang, James J. Xia, Dinggang Shen

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

3 Citations (Scopus)

Abstract

Craniomaxillofacial (CMF) deformities involve congenital and acquired deformities of the head and face. Landmark digitization is a critical step in quantifying CMF deformities. In current clinical practice, CMF landmarks have to be manually digitized on 3D models, which is time-consuming. To date, there is no clinically acceptable method that allows automatic landmark digitization, due to morphological variations among different patients and artifacts of cone-beam computed tomography (CBCT) images. To address these challenges, we propose a segmentation-guided partially-joint regression forest model that can automatically digitizes CMF landmarks. In this model, a regression voting strategy is first adopted to localize landmarks by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, segmentation is also utilized to resolve inconsistent landmark appearances that are caused by morphological variations among different patients, especially on the teeth. Third, a partially-joint model is proposed to separately localize landmarks based on coherence of landmark positions to improve digitization reliability. The experimental results show that the accuracy of automatically digitized landmarks using our approach is clinically acceptable.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages661-668
Number of pages8
Volume9351
ISBN (Print)9783319245737
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 2015 Oct 52015 Oct 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9351
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period15/10/515/10/9

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Zhang, J., Gao, Y., Wang, L., Tang, Z., Xia, J. J., & Shen, D. (2015). Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9351, pp. 661-668). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351). Springer Verlag. https://doi.org/10.1007/978-3-319-24574-4_79