3D medical image segmentation based on 3D convolutional neural networks

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dc.contributor.advisorCuadros Vargas, Alex Jesús
dc.contributor.authorMarquez Herrera, Alejandra
dc.date.accessioned2021-09-29T03:46:21Z
dc.date.available2021-09-29T03:46:21Z
dc.date.issued2021
dc.description.abstractA neural network is a mathematical model that is able to perform a task automatically or semi-automatically after learning the human knowledge that we provided. Moreover, a Convolutional Neural Network (CNN) is a type of sophisticated neural network that has shown to efficiently learn tasks related to the area of image analysis (among other areas). One example of these tasks is image segmentation, which aims to find regions or separable objects within an image. A more specific type of segmentation called semantic segmentation, makes sure that each region has a semantic meaning by giving it a label or class. Since neural networks can automate the task of semantic segmentation of images, they have been very useful for the medical area, applying them to the segmentation of organs or abnormalities (tumors). Therefore, this thesis project seeks to address the task of semantic segmentation of volumetric medical images obtained by Magnetic Resonance Imaging (MRI). Volumetric images are composed of a set of 2D images that altogether represent a volume. We will use a pre-existing Three-dimensional Convolutional Neural Network (3D CNN) architecture, for the binary semantic segmentation of organs in volumetric images. We will talk about the data preprocessing process, as well as specific aspects of the 3D CNN architecture. Finally, we propose a variation in the formulation of the loss function used for training the 3D CNN, also called objective function, for the improvement of pixel-wise segmentation results. We will present the comparisons in performance we made between the proposed loss function and other pre-existing loss functions using two medical image segmentation datasets. es_PE
dc.description.uriTesises_PE
dc.formatapplication/pdfes_PE
dc.identifier.other1073428
dc.identifier.urihttps://hdl.handle.net/20.500.12590/16856
dc.language.isoenges_PE
dc.publisherUniversidad Católica San Pabloes_PE
dc.publisher.countryPEes_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.subjectSemantic Segmentationes_PE
dc.subjectLoss Functiones_PE
dc.subjectMedical Imageses_PE
dc.subjectNeural Networkes_PE
dc.subjectClass Imbalancees_PE
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.02.01es_PE
dc.title3D medical image segmentation based on 3D convolutional neural networkses_PE
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typeinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
renati.advisor.dni29716900
renati.advisor.orcidhttps://orcid.org/0000-0001-7358-9002es_PE
renati.author.dni48477436
renati.discipline611017es_PE
renati.jurorJosé Eduardo Ochoa Lunaes_PE
renati.jurorJean Sequeiraes_PE
renati.jurorGuillermo Cámara Chávezes_PE
renati.jurorJavier Montoya Zegarraes_PE
renati.levelhttps://purl.org/pe-repo/renati/level#maestro
renati.typehttps://purl.org/pe-repo/renati/type#tesis
thesis.degree.disciplineCiencia de la Computaciónes_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ciencia de la Computaciónes_PE
thesis.degree.levelMaestríaes_PE
thesis.degree.nameMaestro en Ciencia de la Computaciónes_PE
thesis.degree.programPrograma Profesional de Ciencia de la Computaciónes_PE
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