Automatic segmentation of a cerebral glioblastoma using a smart computational technique.

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Bibliographic Details
Title:	Automatic segmentation of a cerebral glioblastoma using a smart computational technique.
Alternate Title:	Segmentación automática de glioblastoma cerebral usando una técnica computacional inteligente.
Authors:	Vera, Miguel^1,2 m.avera@unisimonbolivar.edu.co, Huérfano, Yoleidy², Valbuena, Oscar³, Hoyos, Diego⁴, Arias, Yeni⁴, Contreras, Yudith¹, Salazar, Williams⁵, Vera, María Isabel⁵, Borrero, Maryury¹, Vivas, Marisela⁶, Hernández, Carlos¹, Barrera, Doris¹, Molina, Ángel Valentín⁷, Martínez, Luis Javier⁷, Salazar, Juan¹, Gelvez, Elkin¹, Sáenz, Frank⁴
Source:	Archivos Venezolanos de Farmacología y Terapéutica. 10/1/2018, Vol. 37 Issue 4, p336-342. 7p.
Subjects:	IMAGE segmentation, GLIOBLASTOMA multiforme, BRAIN tumors, COMPUTED tomography, ONCOLOGISTS
Abstract (English):	We propose an intelligent computational technique for the image segmentation of a type IV brain tumor, identified as multiform glioblastoma (MGB), which is present in multi-layer computed tomography images. This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and validation. During the validation stage, the Dice coefficient (Dc) is considered in order to compare the segmentations of the MGB, obtained automatically, with the segmentations of the MGB generated manually, by a neuro-oncologist. The combination of parameters linked to the highest Dc, allows to establish the optimal parameters of each of the computational algorithms that make up the proposed nonlinear technique. The obtained results allow to report a Dc higher than 0.88, validating a good correlation between the manual segmentations and those produced by the computational technique developed. [ABSTRACT FROM AUTHOR]
Abstract (Spanish):	Proponemos una técnica computacional inteligente para la segmentación de imágenes de un tumor cerebral tipo IV, identificado como glioblastoma multiforme (MGB), que está presente en imágenes de tomografía computarizada de múltiples capas. Esta técnica consiste en 3 etapas desarrolladas en el dominio tridimensional. Ellos son: preprocesamiento, segmentación y validación. Durante la etapa de validación, se considera el coeficiente de dados (Dc) para comparar las segmentaciones del MGB, obtenidas automáticamente, con las segmentaciones del MGB generado manualmente, por un neurooncólogo. La combinación de parámetros vinculados a la mayor Dc permite establecer los parámetros óptimos de cada uno de los algoritmos computacionales que conforman la técnica no lineal propuesta. Los resultados obtenidos permiten informar una Dc superior a 0,88, validando una buena correlación entre las segmentaciones manuales y las producidas por la técnica computacional desarrollada. [ABSTRACT FROM AUTHOR]
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Database:	MedicLatina

Description
Abstract:	We propose an intelligent computational technique for the image segmentation of a type IV brain tumor, identified as multiform glioblastoma (MGB), which is present in multi-layer computed tomography images. This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and validation. During the validation stage, the Dice coefficient (Dc) is considered in order to compare the segmentations of the MGB, obtained automatically, with the segmentations of the MGB generated manually, by a neuro-oncologist. The combination of parameters linked to the highest Dc, allows to establish the optimal parameters of each of the computational algorithms that make up the proposed nonlinear technique. The obtained results allow to report a Dc higher than 0.88, validating a good correlation between the manual segmentations and those produced by the computational technique developed. [ABSTRACT FROM AUTHOR]
ISSN:	07980264