Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/200775
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dc.contributor.advisorNiñerola Baizán, Aida-
dc.contributor.authorAndrés Rodríguez, Arnau-
dc.date.accessioned2023-07-18T10:40:01Z-
dc.date.available2023-07-18T10:40:01Z-
dc.date.issued2023-06-
dc.identifier.urihttp://hdl.handle.net/2445/200775-
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2023, Tutora: Aida Niñerola Baizanca
dc.description.abstractThis project aims to evaluate deep learning algorithms’ suitability to correctly delineate the regions of interest on computer tomography images for dosimetric computations, in the context of postoperative endometrial carcinoma treatment. To achieve this goal, the project includes the complete training and evaluation of two deep learning networks. Furthermore, a qualitative assessment of the predicted dosimetric computations and a post-processing of the predicted results have been conductedca
dc.format.extent5 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Andrés, 2023-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Física-
dc.subject.classificationAprenentatge profundcat
dc.subject.classificationTomografiacat
dc.subject.classificationCàncer d'endometricat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherDeep learningeng
dc.subject.otherTomographyeng
dc.subject.otherEndometrial cancereng
dc.subject.otherBachelor's theseseng
dc.titleAutomatic segmentation of regions of interest with Deep Learning for postoperative endometrial carcinoma treatmenteng
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Treballs Finals de Grau (TFG) - Física

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