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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43B355H
Repositorysid.inpe.br/sibgrapi/2020/09.27.18.07
Last Update2020:09.28.21.58.13 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.27.18.07.19
Metadata Last Update2022:06.14.00.00.08 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00030
Citation KeySilvaPinhPithOliv:2020:StToSe
TitleA study on tooth segmentation and numbering using end-to-end deep neural networks
FormatOn-line
Year2020
Access Date2024, Apr. 27
Number of Files1
Size3515 KiB
2. Context
Author1 Silva, Bernardo Peters Menezes
2 Pinheiro, Laís Bastos
3 Pithon, Matheus Melo
4 Oliveira, Luciano Rebouças de
Affiliation1 Universidade Federal da Bahia
2 Universidade Federal da Bahia
3 Universidade Estadual do Sudoeste da Bahia
4 Universidade Federal da Bahia
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressbpmsilva@gmail.com
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-28 21:58:13 :: bpmsilva@gmail.com -> administrator :: 2020
2022-06-14 00:00:08 :: administrator -> bpmsilva@gmail.com :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsdeep neural networks
instance segmentation and numbering
panoramic dental X-rays
AbstractShape, number, and position of teeth are the main targets of a dentist when screening for patient's problems on X-rays. Rather than solely relying on the trained eyes of the dentists, computational tools have been proposed to aid specialists as decision supporter for better diagnoses. When applied to X-rays, these tools are specially grounded on object segmentation and detection. In fact, the very first goal of segmenting and detecting the teeth in the images is to facilitate other automatic methods in further processing steps. Although researches over tooth segmentation and detection are not recent, the application of deep learning techniques in the field is new and has not reached maturity yet. To fill some gaps in the area of dental image analysis, we bring a thorough study on tooth segmentation and numbering on panoramic X-ray images by means of end-to-end deep neural networks. For that, we analyze the performance of four network architectures, namely, Mask R-CNN, PANet, HTC, and ResNeSt, over a challenging data set. The choice of these networks was made upon their high performance over other data sets for instance segmentation and detection. To the best of our knowledge, this is the first study on instance segmentation, detection, and numbering of teeth on panoramic dental X-rays. We found that (i) it is completely feasible to detect, to segment, and to number teeth by through any of the analyzed architectures, (ii) performance can be significantly boosted with the proper choice of neural network architecture, and (iii) the PANet had the best results on our evaluations with an mAP of 71.3% on segmentation and 74.0% on numbering, raising 4.9 and 3.5 percentage points the results obtained with Mask R-CNN.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > A study on...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A study on...
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paper-camera-ready-final.pdf 27/09/2020 15:07 3.4 MiB
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/43B355H
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43B355H
Languageen
Target Filepaper-camera-ready-final-com-acento.pdf
User Groupbpmsilva@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 3
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Description control
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