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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/4BFC5SP
Repositorysid.inpe.br/sibgrapi/2024/06.14.00.26
Last Update2024:06.14.00.27.03 (UTC) gumartinslopes@gmail.com
Metadata Repositorysid.inpe.br/sibgrapi/2024/06.14.00.27
Metadata Last Update2024:06.21.01.38.42 (UTC) administrator
Citation KeyCostaRoFoJrSoGu:2023:SiObDe
TitleSingle-Shot Object Detection and Supervised Image Segmentation for Analysing Cell Images Obtainedby Immunohistochemistry
Short TitleSingle-Shot Object Detection and Supervised Image Segmentation for Analysing Cell Images Obtainedby Immunohistochemistry
FormatOn-line
Year2023
Access Date2024, Sep. 08
Number of Files1
Size1612 KiB
2. Context
Author1 Costa, Gustavo Martins Lopes da
2 Rodrigues, Anna P. C.
3 Fonseca, Gabriel Barbosa da
4 Jr, Zenilton K. G. do Patrocínio
5 Souto, Giovanna Ribeiro
6 Guimarăes, Silvio Jamil F.
Affiliation1 PUC Minas
2 PUC Minas
3 PUC Minas
4 PUC Minas
5 PUC Minas
6 PUC Minas
EditorClua, Esteban Walter Gonzalez
Körting, Thales Sehn
Paulovich, Fernando Vieira
Feris, Rogerio
e-Mail Addressgumartinslopes@gmail.com
Conference NameConference on Graphics, Patterns and Images, 36 (SIBGRAPI)
Conference LocationRio Grande, RS
DateNov. 06-09, 2023
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2024-06-14 00:27:08 :: gumartinslopes@gmail.com -> administrator ::
2024-06-21 01:38:42 :: administrator -> :: 2023
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsImage Segmentation
Cell Detection
Computer Vision
Machine Learning
Immunohistochemistry
AbstractAnalyzing cell images and identifying them correctly is a fundamental task in the immunohistochemical exam. In this paper we propose a novel method to segment FoxP3+ Regulatory T cells (Treg) images automatically, in order to assist healthcare professionals in the task of identifying and counting potentially cancerous cells. The proposed method relies on combining an object detection network, which is tailor-made for microscopy images, with a marker-based image segmentation method to produce the final segmentation, while requiring only a 50x50 training patch to do so. Our pipeline consists on predicting the location of the cells, applying morphological operations on the prediction weights to transform them into markers, and finally using the segmentation method iDISF to generate high quality segmentations. We also propose a new FoxP3+ Treg cells dataset containing 10 high resolution images, with a qualitative and quantitative analysis of our segmentation methods for this dataset.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2023 > Single-Shot Object Detection and Supervised Image Segmentation for Analysing Cell Images Obtainedby Immunohistochemistry
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4. Conditions of access and use
data URLhttp://sibgrapi.sid.inpe.br/ibi/8JMKD3MGPEW34M/4BFC5SP
zipped data URLhttp://sibgrapi.sid.inpe.br/zip/8JMKD3MGPEW34M/4BFC5SP
Languageen
Target FileSingle-Shot Object Detection and Supervised ImageSegmentation for Analysing Cell Images Obtainedby Immunohistochemistry.pdf
User Groupgumartinslopes@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/4BG6FTP
Citing Item Listsid.inpe.br/sibgrapi/2024/06.18.21.17 19
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 holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session sponsor subject tertiarymark type url versiontype volume


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