Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model and Multiscale Statistical Features
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Jun | - |
dc.contributor.author | Gao, Yaozong | - |
dc.contributor.author | Wang, Li | - |
dc.contributor.author | Tang, Zhen | - |
dc.contributor.author | Xia, James J. | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-03T20:36:45Z | - |
dc.date.available | 2021-09-03T20:36:45Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2016-09 | - |
dc.identifier.issn | 0018-9294 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/87688 | - |
dc.description.abstract | Objective: The goal of this paper is to automatically digitize craniomaxillofacial (CMF) landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images. Methods: We propose a segmentation-guided partially-joint regression forest (S-PRF) model to automatically digitize CMF landmarks. In this model, a regression voting strategy is first adopted to localize each landmark by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, CBCT image segmentation is utilized to remove uninformative voxels caused by morphological variations across patients. Third, a partially-joint model is further proposed to separately localize landmarks based on the coherence of landmark positions to improve the digitization reliability. In addition, we propose a fast vector quantization method to extract high-level multiscale statistical features to describe a voxel's appearance, which has low dimensionality, high efficiency, and is also invariant to the local inhomogeneity caused by artifacts. Results: Mean digitization errors for 15 landmarks, in comparison to the ground truth, are all less than 2 mm. Conclusion: Our model has addressed challenges of both interpatient morphological variations and imaging artifacts. Experiments on a CBCT dataset show that our approach achieves clinically acceptable accuracy for landmark digitalization. Significance: Our automatic landmark digitization method can be used clinically to reduce the labor cost and also improve digitalization consistency. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | TEXTURE CLASSIFICATION | - |
dc.subject | LOCALIZATION | - |
dc.subject | INTEGRATION | - |
dc.subject | SCALE | - |
dc.subject | TREES | - |
dc.title | Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model and Multiscale Statistical Features | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1109/TBME.2015.2503421 | - |
dc.identifier.scopusid | 2-s2.0-84984829683 | - |
dc.identifier.wosid | 000382677500005 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.63, no.9, pp.1820 - 1829 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | - |
dc.citation.title | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | - |
dc.citation.volume | 63 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 1820 | - |
dc.citation.endPage | 1829 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.subject.keywordPlus | TEXTURE CLASSIFICATION | - |
dc.subject.keywordPlus | LOCALIZATION | - |
dc.subject.keywordPlus | INTEGRATION | - |
dc.subject.keywordPlus | SCALE | - |
dc.subject.keywordPlus | TREES | - |
dc.subject.keywordAuthor | Cone-beam computed tomography (CBCT) | - |
dc.subject.keywordAuthor | fast vector quantization (VQ) | - |
dc.subject.keywordAuthor | landmark digitization | - |
dc.subject.keywordAuthor | partially-joint regression forest (PRF) | - |
dc.subject.keywordAuthor | segmentation | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.