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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45EDUCE
Repositorysid.inpe.br/sibgrapi/2021/09.15.19.59
Last Update2021:09.15.19.59.11 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.15.19.59.11
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyDinizSilvPaiv:2021:MeSeSp
TitleMethods for segmentation of spinal cord and esophagus in radiotherapy planning computed tomography
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size1302 KiB
2. Context
Author1 Diniz, João Otávio Bandeira
2 Silva, Aristófanes Corrêa
3 Paiva, Anselmo Cardoso de
Affiliation1 Instituto Federal de Educação, Ciência e Tecnologia do Maranhão
2 Universidade Federal do Maranhão
3 Universidade Federal do Maranhão
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressjoao.obd@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2021-09-15 19:59:11 :: joao.obd@gmail.com -> administrator ::
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsComputed Tomography
Esophagus
Spinal Cord
OAR
Deep Learning
AbstractOrgans at Risk (OARs) are healthy tissues around cancer that must be preserved in radiotherapy (RT). The spinal cord and esophagus are crucial OARs. In this work, we proposed methods for the segmentation of these OARs from the CT using image processing techniques and deep convolutional neural network (CNN). For spinal cord segmentation, two methods are proposed, the first using techniques such as template matching, superpixel, and CNN. The second method, use adaptive template matching and CNN. In the esophagus segmentation, we proposed a method composed of registration techniques, atlas, pre-processing, U-Net, and post-processing. The methods were applied to 36 planning CT images provided by The Cancer Imaging Archive. The first method for spinal cord segmentation obtained 78.20\% Dice. The second method for spinal cord segmentation obtained 81.69\% Dice. The esophagus segmentation method obtained an accuracy of 82.15\% Dice.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45EDUCE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45EDUCE
Languageen
Target Filepaper.pdf
User Groupjoao.obd@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 5
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi 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 versiontype volume


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