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

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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, Miguel1,2 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]
Copyright of Archivos Venezolanos de Farmacología y Terapéutica is the property of Archivos Venezolanos de Farmacologia y Terapeutica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: MedicLatina
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PubType: Academic Journal
PubTypeId: academicJournal
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  Group: Ti
  Data: Automatic segmentation of a cerebral glioblastoma using a smart computational technique.
– Name: TitleAlt
  Label: Alternate Title
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  Data: Segmentación automática de glioblastoma cerebral usando una técnica computacional inteligente.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Vera%2C+Miguel%22">Vera, Miguel</searchLink><relatesTo>1,2</relatesTo><i> m.avera@unisimonbolivar.edu.co</i><br /><searchLink fieldCode="AR" term="%22Huérfano%2C+Yoleidy%22">Huérfano, Yoleidy</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Valbuena%2C+Oscar%22">Valbuena, Oscar</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Hoyos%2C+Diego%22">Hoyos, Diego</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Arias%2C+Yeni%22">Arias, Yeni</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Contreras%2C+Yudith%22">Contreras, Yudith</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Salazar%2C+Williams%22">Salazar, Williams</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Vera%2C+María+Isabel%22">Vera, María Isabel</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Borrero%2C+Maryury%22">Borrero, Maryury</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Vivas%2C+Marisela%22">Vivas, Marisela</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Hernández%2C+Carlos%22">Hernández, Carlos</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Barrera%2C+Doris%22">Barrera, Doris</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Molina%2C+Ángel+Valentín%22">Molina, Ángel Valentín</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Martínez%2C+Luis+Javier%22">Martínez, Luis Javier</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Salazar%2C+Juan%22">Salazar, Juan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gelvez%2C+Elkin%22">Gelvez, Elkin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Sáenz%2C+Frank%22">Sáenz, Frank</searchLink><relatesTo>4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Archivos+Venezolanos+de+Farmacología+y+Terapéutica%22">Archivos Venezolanos de Farmacología y Terapéutica</searchLink>. 10/1/2018, Vol. 37 Issue 4, p336-342. 7p.
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  Data: <searchLink fieldCode="DE" term="%22IMAGE+segmentation%22">IMAGE segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22GLIOBLASTOMA+multiforme%22">GLIOBLASTOMA multiforme</searchLink><br /><searchLink fieldCode="DE" term="%22BRAIN+tumors%22">BRAIN tumors</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTED+tomography%22">COMPUTED tomography</searchLink><br /><searchLink fieldCode="DE" term="%22ONCOLOGISTS%22">ONCOLOGISTS</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: 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]
– Name: Abstract
  Label: Abstract (Spanish)
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Archivos Venezolanos de Farmacología y Terapéutica is the property of Archivos Venezolanos de Farmacologia y Terapeutica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 7
        StartPage: 336
    Subjects:
      – SubjectFull: IMAGE segmentation
        Type: general
      – SubjectFull: GLIOBLASTOMA multiforme
        Type: general
      – SubjectFull: BRAIN tumors
        Type: general
      – SubjectFull: COMPUTED tomography
        Type: general
      – SubjectFull: ONCOLOGISTS
        Type: general
    Titles:
      – TitleFull: Automatic segmentation of a cerebral glioblastoma using a smart computational technique.
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              Text: 10/1/2018
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