Tooth instance segmentation from cone-beam CT images through point-based detection and Gaussian disentanglement
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jusang | - |
dc.contributor.author | Chung, Minyoung | - |
dc.contributor.author | Lee, Minkyung | - |
dc.contributor.author | Shin, Yeong-Gil | - |
dc.date.accessioned | 2022-05-16T00:40:03Z | - |
dc.date.available | 2022-05-16T00:40:03Z | - |
dc.date.created | 2022-05-16 | - |
dc.date.issued | 2022-05 | - |
dc.identifier.issn | 1380-7501 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/42354 | - |
dc.description.abstract | Individual tooth segmentation and identification from cone-beam computed tomography images are preoperative prerequisites for orthodontic treatments. Instance segmentation methods using convolutional neural networks have demonstrated ground-breaking results on individual tooth segmentation tasks, and are used in various medical imaging applications. While point-based detection networks achieve superior results on dental images, it is still a challenging task to distinguish adjacent teeth because of their similar topologies and proximate nature. In this study, we propose a point-based tooth localization network that effectively disentangles each individual tooth based on a Gaussian disentanglement objective function. The proposed network first performs heatmap regression accompanied by box regression for all the anatomical teeth. A novel Gaussian disentanglement penalty is employed by minimizing the sum of the pixel-wise multiplication of the heatmaps for all adjacent teeth pairs. Subsequently, individual tooth segmentation is performed by converting a pixel-wise labeling task to a distance map regression task to minimize false positives in adjacent regions of the teeth. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of detection by 9.1%, which results in a high performance in terms of individual tooth segmentation. The primary significance of the proposed method is two-fold: (1) the introduction of a point-based tooth detection framework that does not require additional classification and (2) the design of a novel loss function that effectively separates Gaussian distributions based on heatmap responses in the point-based detection framework. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | MULTIMEDIA TOOLS AND APPLICATIONS | - |
dc.title | Tooth instance segmentation from cone-beam CT images through point-based detection and Gaussian disentanglement | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s11042-022-12524-9 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | MULTIMEDIA TOOLS AND APPLICATIONS, v.81, no.13, pp.18327 - 18342 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000766430800007 | - |
dc.identifier.scopusid | 2-s2.0-85126071807 | - |
dc.citation.endPage | 18342 | - |
dc.citation.number | 13 | - |
dc.citation.startPage | 18327 | - |
dc.citation.title | MULTIMEDIA TOOLS AND APPLICATIONS | - |
dc.citation.volume | 81 | - |
dc.contributor.affiliatedAuthor | Chung, Minyoung | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Distance-based segmentation | - |
dc.subject.keywordAuthor | Gaussian disentanglement loss | - |
dc.subject.keywordAuthor | Instance segmentation | - |
dc.subject.keywordAuthor | Point-based object detection | - |
dc.subject.keywordAuthor | Tooth CBCT segmentation | - |
dc.subject.keywordPlus | FEATURES | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Soongsil University Library 369 Sangdo-Ro, Dongjak-Gu, Seoul, Korea (06978)02-820-0733
COPYRIGHT ⓒ SOONGSIL 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.