Buscar en Google Scholar
Registro completo de metadatos
Cuadros Vargas, Alex Jesús
Marquez Herrera, Alejandra
2021-09-29T03:46:21Z
2021-09-29T03:46:21Z
2021
1073428
https://hdl.handle.net/20.500.12590/16856
A 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)
Tesis (es_PE)
application/pdf (es_PE)
eng (es_PE)
Universidad Católica San Pablo (es_PE)
info:eu-repo/semantics/openAccess (es_PE)
https://creativecommons.org/licenses/by/4.0/ (es_PE)
Universidad Católica San Pablo (es_PE)
Repositorio Institucional - UCSP (es_PE)
Semantic Segmentation (es_PE)
Loss Function (es_PE)
Medical Images (es_PE)
Neural Network (es_PE)
Class Imbalance (es_PE)
3D medical image segmentation based on 3D convolutional neural networks (es_PE)
info:eu-repo/semantics/masterThesis (es_PE)
info:eu-repo/semantics/masterThesis (es_PE)
Universidad Católica San Pablo. Departamento de Ciencia de la Computación (es_PE)
Ciencia de la Computación (es_PE)
Maestría (es_PE)
Maestro en Ciencia de la Computación (es_PE)
PE (es_PE)
http://purl.org/pe-repo/ocde/ford#1.02.01 (es_PE)
Programa Profesional de Ciencia de la Computación (es_PE)
https://purl.org/pe-repo/renati/level#maestro (es_PE)
29716900
https://orcid.org/0000-0001-7358-9002 (es_PE)
48477436
611017 (es_PE)
José Eduardo Ochoa Luna (es_PE)
Jean Sequeira (es_PE)
Guillermo Cámara Chávez (es_PE)
Javier Montoya Zegarra (es_PE)
https://purl.org/pe-repo/renati/type#tesis (es_PE)
Privada asociativa
info:eu-repo/semantics/publishedVersion (es_PE)



Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons