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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12590/16856
Title: 3D medical image segmentation based on 3D convolutional neural networks
Authors: Marquez Herrera, Alejandra
metadata.dc.contributor.advisor: Cuadros Vargas, Alex Jesús
Keywords: Semantic Segmentation;Loss Function;Medical Images;Neural Network;Class Imbalance
Issue Date: 2021
Publisher: Universidad Católica San Pablo
Abstract: 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.
URI: http://hdl.handle.net/20.500.12590/16856
Appears in Collections:Tesis Pregrado - Ciencia de la computación

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