Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps

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dc.contributor.author Nicolás-Sáenz, Laura
dc.contributor.author Guerrero Aspizua, Sara
dc.contributor.author Pascau González-Garzón, Javier
dc.contributor.author Muñoz Barrutia, María Arrate
dc.date.accessioned 2021-03-02T10:29:08Z
dc.date.available 2021-03-02T10:29:08Z
dc.date.issued 2020-09
dc.identifier.bibliographicCitation Nicolás-Sáenz, L., Guerrero-Aspizua, S., Pascau, J., Muñoz-Barrutia, A. (2020). Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps. Entropy, 22(9), 946.
dc.identifier.issn 1099-4300
dc.identifier.uri http://hdl.handle.net/10016/32066
dc.description.abstract We present a novel method to assess the variations in protein expression and spatial heterogeneity of tumor biopsies with application in computational pathology. This was done using different antigen stains for each tissue section and proceeding with a complex image registration followed by a final step of color segmentation to detect the exact location of the proteins of interest. For proper assessment, the registration needs to be highly accurate for the careful study of the antigen patterns. However, accurate registration of histopathological images comes with three main problems: the high amount of artifacts due to the complex biopsy preparation, the size of the images, and the complexity of the local morphology. Our method manages to achieve an accurate registration of the tissue cuts and segmentation of the positive antigen areas.
dc.format.extent 19
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2020 by the authors.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Computational pathology
dc.subject.other Image registration
dc.subject.other Antigen segmentation
dc.subject.other Cancer
dc.title Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps
dc.type article
dc.subject.eciencia Biología y Biomedicina
dc.identifier.doi https://doi.org/10.3390/e22090946
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2016-78052-R
dc.relation.projectID Gobierno de España. TEC2015-73064-EXP
dc.relation.projectID Comunidad de Madrid. IND2018/TIC-9753
dc.relation.projectID Gobierno de España. RTC-2017-6600-1
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 9
dc.identifier.publicationlastpage 19
dc.identifier.publicationtitle Entropy
dc.identifier.publicationvolume 22
dc.identifier.uxxi AR/0000026498
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Ciencia, Innovación y Universidades (España)
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