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Re-Identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks

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dc.contributor.author Rafael-Palou, Xavier
dc.contributor.author Aubanell, Anton
dc.contributor.author Bonavita, Ilaria
dc.contributor.author Ceresa, Mario
dc.contributor.author Piella Fenoy, Gemma
dc.contributor.author Ribas, Vicent
dc.contributor.author González Ballester, Miguel Ángel, 1973-
dc.date.accessioned 2021-02-12T07:29:53Z
dc.date.issued 2020
dc.identifier.citation Rafael-Palou X, Aubanell A, Bonavita I, Ceresa M, Piella G, Ribas V, González Ballester MA. Re-Identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks. Med Image Anal. 2020 Oct 7;67:101823. DOI: 10.1016/j.media.2020.101823
dc.identifier.issn 1361-8415
dc.identifier.uri http://hdl.handle.net/10230/46460
dc.description.abstract Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient need to be individually inspected, looking for possible cancerogenous nodules. Radiologists mainly focus their attention in nodule size, density, and growth to assess the existence of malignancy. In this study, we present a novel method based on a 3D siamese neural network, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration. The network was integrated into a two-stage automatic pipeline to detect, match, and predict nodule growth given pairs of CT scans. Results on an independent test set reported a nodule detection sensitivity of 94.7%, an accuracy for temporal nodule matching of 88.8%, and a sensitivity of 92.0% with a precision of 88.4% for nodule growth detection.
dc.description.sponsorship This work was partially funded by the Industrial Doctorates Program (AGAUR) grant number DI087, and the Spanish Ministry of Economy and Competitiveness (Project INSPIRE FIS2017-89535-C2-2-R, Maria de Maeztu Units of Excellence Program MDM-2015-0502).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Medical Image Analysis. 2020 Oct 7;67:101823.
dc.rights © Elsevier http://dx.doi.org/10.1016/j.media.2020.101823
dc.title Re-Identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.media.2020.101823
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/FIS2017-89535-C2-2-R
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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