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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45EHMTL
Repositorysid.inpe.br/sibgrapi/2021/09.16.16.33
Last Update2021:09.30.12.39.29 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.16.16.33.49
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyPaulaSalv:2021:BrToRe
TitleBreast Tomosynthesis Reconstruction Using Artificial Neural Networks with Deep Learning
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size2529 KiB
2. Context
Author1 Paula, Davi Duarte de
2 Salvadeo, Denis Henrique Pinheiro
Affiliation1 São Paulo State University (Unesp) - Institute of Geosciences and Exact Sciences
2 São Paulo State University (Unesp) - Institute of Geosciences and Exact Sciences
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 Addressdavi.duarte@unesp.br
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)2021-09-30 12:39:30 :: davi.duarte@unesp.br -> administrator :: 2021
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsDeep Learning
Breast Tomosynthesis
Image Reconstruction
AbstractThe Filtered Backprojection (FBP) algorithm for Computed Tomography (CT) reconstruction can be mapped entire in an Artificial Neural Network (ANN), with the backprojection (BP) operation simulated analytically in a layer and the Ram-Lak filter simulated as a convolutional layer. Thus, this work adapt the BP layer for DBT reconstruction, making possible the use of FBP simulated as a ANN to reconstruct DBT images. For evaluation, Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics were calculated to measure the improvement of the images made by the ANN, regarding a dataset containing 100 virtual breast phantoms to perform the experiments. We shown that making the Ram-Lak layer trainable, the reconstructed image can be improved in terms of noise reduction. And, considering an additional post-filtering step performed by Denoising Convolutional Neural Network (DnCNN), it shown comparable and superior results than a state-of-the-art DBT reconstruction method, averaging 37.644 dB and 0.869 values of PSNR and SSIM, respectively. Finally, this study enables additional proposals of ANN with Deep Learning models for DBT reconstruction and denoising.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Breast Tomosynthesis Reconstruction...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45EHMTL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45EHMTL
Languageen
Target Fileartigo_final.pdf
User Groupdavi.duarte@unesp.br
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 6
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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