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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RNF7US
Repositorysid.inpe.br/sibgrapi/2018/08.29.19.07
Last Update2018:08.29.19.07.29 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/08.29.19.07.29
Metadata Last Update2022:06.14.00.09.11 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00058
Citation KeyJaderFonRuiPitOli:2018:DeInSe
TitleDeep instance segmentation of teeth in panoramic X-ray images
FormatOn-line
Year2018
Access Date2024, Apr. 30
Number of Files1
Size1860 KiB
2. Context
Author1 Jader, Gil
2 Fontinele, Jefferson
3 Ruiz, Marco
4 Pithon, Matheus
5 Oliveira, Luciano
Affiliation1 UFBA
2 UFBA
3 UFBA
4 UESC
5 UFBA
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addresslrebouca@ufba.br
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-08-29 19:07:29 :: lrebouca@ufba.br -> administrator ::
2022-06-14 00:09:11 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsinstance segmentation
tooth segmentation
panoramic X-ray image
AbstractIn dentistry, radiological examinations help specialists by showing structure of the tooth bones with the goal of screening embedded teeth, bone abnormalities, cysts, tumors, infections, fractures, problems in the temporomandibular regions, just to cite a few. Sometimes, relying solely in the specialist's opinion can bring differences in the diagnoses, which can ultimately hinder the treatment. Although tools for complete automatic diagnosis are no yet expected, image pattern recognition has evolved towards decision support, mainly starting with the detection of teeth and their components in X-ray images. Tooth detection has been object of research during at least the last two decades, mainly relying in threshold and region-based methods. Following a different direction, this paper proposes to explore a deep learning method for instance segmentation of the teeth. To the best of our knowledge, it is the first system that detects and segment each tooth in panoramic X-ray images. It is noteworthy that this image type is the most challenging one to isolate teeth, since it shows other parts of patient's body (e.g., chin, spine and jaws). We propose a segmentation system based on mask region-based convolutional neural network to accomplish an instance segmentation. Performance was thoroughly assessed from a 1500 challenging image data set, with high variation and containing 10 categories of different types of buccal image. By training the proposed system with only 193 images of mouth containing 32 teeth in average, using transfer learning strategies, we achieved 98% of accuracy, 88% of F1-score, 94% of precision, 84% of recall and 99% of specificity over 1224 unseen images, results very superior than other 10 unsupervised methods.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RNF7US
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RNF7US
Languageen
Target Filetooth_segmentation.pdf
User Grouplrebouca@ufba.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 8
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


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