Improving semantic segmentation of 3D medical images on 3D convolutional neural networks Herrera A.M. es_PE Cuadros-Vargas A.J. es_PE Pedrini H. es_PE 2024-05-30T23:13:38Z 2024-05-30T23:13:38Z 2019
dc.description.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 neural network that has shown to efficiently learn tasks related to the area of image analysis, such as image segmentation, whose main purpose is to find regions or separable objects within an image. A more specific type of segmentation, called semantic segmentation, guarantees that each region has a semantic meaning by giving it a label or class. Since CNNs can automate the task of image semantic segmentation, they have been very useful for the medical area, applying them to the segmentation of organs or abnormalities (tumors). This work aims to improve the task of binary semantic segmentation of volumetric medical images acquired by Magnetic Resonance Imaging (MRI) using a preexisting Three-Dimensional Convolutional Neural Network (3D CNN) architecture. We propose a formulation of a loss function for training this 3D CNN, for improving pixel-wise segmentation results. This loss function is formulated based on the idea of adapting a similarity coefficient, used for measuring the spatial overlap between the prediction and ground truth, and then using it to train the network. As contribution, the developed approach achieved good performance in a context where the pixel classes are imbalanced. We show how the choice of the loss function for training can affect the final quality of the segmentation. We validate our proposal over two medical image semantic segmentation datasets and show comparisons in performance between the proposed loss function and other pre-existing loss functions used for binary semantic segmentation. © 2019 IEEE.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.scopus 2-s2.0-85084746854
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof Proceedings - 2019 45th Latin American Computing Conference, CLEI 2019
dc.rights info:eu-repo/semantics/openAccess
dc.subject Semantic Segmentation
dc.subject Class Imbalance es_PE
dc.subject Convolutional Neural Network es_PE
dc.subject Loss Function es_PE
dc.subject Medical Images es_PE
dc.title Improving semantic segmentation of 3D medical images on 3D convolutional neural networks
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication