A novel in silico platform for a fully automatic personalized brain tumor growth.
Magn Reson Imaging
; 68: 121-126, 2020 05.
Article
em En
| MEDLINE
| ID: mdl-31911200
ABSTRACT
Glioblastoma Multiforme is the most common and most aggressive type of brain tumors. Although accurate prediction of Glioblastoma borders and shape is absolutely essential for neurosurgeons, there are not many in silico platforms that can make such predictions. In the current study, an automatic patient-specific simulation of Glioblastoma growth would be described. A finite element approach is used to analyze the magnetic resonance images from patients in the early stages of their tumors. For segmentation of the tumor, the Support Vector Machine (SVM) method, which is an automatic segmentation algorithm, is used. Using in situ and in vivo data, the main parameters of tumor prediction and growth are estimated with high precision in proliferation-invasion partial differential equation, using the genetic algorithm optimization method. The results show that for a C57BL mouse, the differences between the area and perimeter of in vivo test and simulation prediction data, as the objective function, are 3.7% and 17.4%, respectively.
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MEDLINE
Assunto principal:
Simulação por Computador
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Processamento de Imagem Assistida por Computador
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Encéfalo
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Neoplasias Encefálicas
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Glioblastoma
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article