Automatic segmentation of a cerebral glioblastoma using a smart computational technique
| dc.contributor.author | Vera, Miguel | |
| dc.contributor.author | Huérfano, Yoleidy | |
| dc.contributor.author | Valbuena, Oscar | |
| dc.contributor.author | Hoyos, Diego | |
| dc.contributor.author | Arias, Yeni | |
| dc.contributor.author | Contreras, Yudith | |
| dc.contributor.author | Salazar, Williams | |
| dc.contributor.author | Vera, María Isabel | |
| dc.contributor.author | Borrero, Maryury | |
| dc.contributor.author | Vivas, Marisela | |
| dc.contributor.author | Hernández, Carlos | |
| dc.contributor.author | Barrera, Doris | |
| dc.contributor.author | Molina, Ángel Valentín | |
| dc.contributor.author | Martínez, Luis Javier | |
| dc.contributor.author | Salazar, Juan | |
| dc.contributor.author | Gelvez, Elkin | |
| dc.date.accessioned | 2019-01-25T14:52:01Z | |
| dc.date.available | 2019-01-25T14:52:01Z | |
| dc.date.issued | 2018 | |
| dc.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. | eng |
| dc.description.abstract | 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. | spa |
| dc.identifier.issn | 26107988 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12442/2524 | |
| dc.language.iso | eng | eng |
| dc.publisher | Sociedad Venezolana de Farmacología Clínica y Terapéutica | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.source | Revista AVFT-Archivos Venezolanos de Farmacología y Terapéutica | spa |
| dc.source | Vol. 37, No. 4 (2018) | spa |
| dc.source.uri | http://www.revistaavft.com/images/revistas/2018/avft_4_2018/5_automatic_segmentation_of_a_cerebral.pdf | eng |
| dc.subject | Brain Tomography | eng |
| dc.subject | Cerebral Tumor | eng |
| dc.subject | Glioblastoma | eng |
| dc.subject | Intelligent Computational Technique | eng |
| dc.subject | Segmentation | eng |
| dc.subject | Tomografía cerebral | spa |
| dc.subject | Tumor cerebral | spa |
| dc.subject | Glioblastoma | spa |
| dc.subject | Técnica computacional inteligente | spa |
| dc.subject | Segmentación | spa |
| dc.title | Automatic segmentation of a cerebral glioblastoma using a smart computational technique | eng |
| dc.title.alternative | Segmentación automática de glioblastoma cerebral usando una técnica computacional inteligente | spa |
| dc.type | article | eng |
| dcterms.references | Stelzer K. Epidemiology and prognosis of brain metastases. Surg Neurol Int. 2013;4(Suppl 4):S192-202. | eng |
| dcterms.references | Mcneill KA. Epidemiology of Brain Tumors. Neurol Clin. 2016;34(4):981-998. | eng |
| dcterms.references | American Brain Tumor Association (ABTA). About Brain Tumors: A Primer for Patients and Caregivers. 9ª Edition. 2015 ABTA. | eng |
| dcterms.references | WHO (2007). Cavenee W, Louis D, Ohgaki H et al. Eds. WHO Classification of Tumours of the Central Nervous System. WHO Regional Office Europe. | eng |
| dcterms.references | Ostrom Q., Gittleman H., Xi J., Kromer C., Wolinsky Y., Krinchko C., Barnholtz J., CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2009- 2013, Neuro Oncol (2016) 18 (suppl 5) v1-v75. | eng |
| dcterms.references | Vera M. Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi-corte. Ph.D. dissertation, Universidad de los Andes, Mérida-Venezuela, 2014. | spa |
| dcterms.references | Maiera A, Wigstrm L, Hofmann H, Hornegger J, Zhu L, Strobel N, Fahrig R. Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT. Medical Physics. 2011;38(11):5896–909. | eng |
| dcterms.references | Kroft L, De Roos A, Geleijns J. Artifacts in ECG– synchronized MDCT coronary angiography. American Journal of Roentgenology. 2007;189(3):581–91. | eng |
| dcterms.references | Casamitjana A., Puch S., Aduriz A., Vilaplana V. (2017). 3D Convolutional Neural Networks for Brain Tumor Segmentation: a comparison of multi-resolution architectures. arXiv:1705.08236v1. | eng |
| dcterms.references | Zhang, J., Shen, X., Zhuo, T., & Zhou, H. (2017). Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss. arXiv preprint arXiv:1712.09093 | eng |
| dcterms.references | Kleesiek, J., Biller, A., Urban, G., Kothe, U., Bendszus, M., & Hamprecht, F. (2014). Ilastik for multi-modal brain tumor segmentation. Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), 12-17. | eng |
| dcterms.references | Serra J. Image Analysis Using Mathematical Morphology. London, England: Academic Press, 1982. | eng |
| dcterms.references | W. Pratt, Digital Image Processing. USA: John Wiley & Sons Inc, 2007. | eng |
| dcterms.references | Mukhopadhyay S., Chanda B. A multiscale morphological approach to local contrast enhancement. Signal Processing, vol. 80, no. 4, pp. 685–696, 2000. | eng |
| dcterms.references | Yu Z., Wei G., Zhen C., Jing T., Ling L. Medical images edge detection based on mathematical morphology. En Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai–China, September 2005, pp. 6492–6495. | eng |
| dcterms.references | Sezgin M., Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, vol. 13, pp. 146–165, 2004. | eng |
| dcterms.references | Meijering H. Image en enhancement in digital X ray angiography. Tesis de Doctorado, Utrecht University, Netherlands, 2000. | eng |
| dcterms.references | V. Vapnik, Statistical Learning Theory. New York: John Wiley & Sons, 1998. | eng |
| dcterms.references | E. Osuna, R. Freund, y F. Girosi, “Training support vector machines: an application to face detection.” en Conference on Computer Vision and Pattern Recognition (CVPR ’97), San Juan, Puerto Rico, 1997, pp. 130–136. | eng |
| dcterms.references | A. Smola, “Learning with kernels,” Tesis de Doctorado,Technische Universitt Berlin, Germany, 1998. | eng |
| dcterms.references | B. Scholkopf y A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA , USA: The MIT Press, 2002. | eng |
| dcterms.references | J. Suykens, T. V. Gestel, y J. D. Brabanter, Least Squares Support Vector Machines.UK: World Scientific Publishing Co., 2002. | eng |
| dcterms.references | M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, y T. Poggio, “Pedestrian detection using wavelet templates,” en CVPR ’97: Conference on Computer Vision and Pattern Recognition (CVPR ’97). Washington, DC, USA: IEEE Computer Society, 1997, pp. 193–200. | eng |