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Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

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Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

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dc.contributor.author Juan Albarracín, Javier es_ES
dc.contributor.author Fuster García, Elíes es_ES
dc.contributor.author Manjón Herrera, José Vicente es_ES
dc.contributor.author Robles Viejo, Montserrat es_ES
dc.contributor.author Aparici, F. es_ES
dc.contributor.author Marti-Bonmati, L. es_ES
dc.contributor.author García Gómez, Juan Miguel es_ES
dc.date.accessioned 2016-05-17T11:04:00Z
dc.date.available 2016-05-17T11:04:00Z
dc.date.issued 2015-05-15
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10251/64235
dc.description.abstract Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. es_ES
dc.description.sponsorship EFG was supported by Programa Torres Quevedo, Ministerio de Educacion y Ciencia, co-funded by the European Social Fund (PTQ-1205693). EFG, JMGG, and JVM were supported by Red Tematica de Investigacion Cooperativa en Cancer, (RTICC) 2013-2016 (RD12/0036/0020). JMGG was supported by Project TIN2013-43457-R: Caracterizacion de firmas biologicas de glioblastomas mediante modelos no-supervisados de prediccion estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economia y Competitividad of Spain; CON2014001 UPV-IISLaFe: Unsupervised glioblastoma tumor components segmentation based on perfusion multiparametric MRI and spatio/temporal constraints; and CON2014002 UPV-IISLaFe: Empleo de segmentacion no supervisada multiparametrica basada en perfusion RM para la caracterizacion del edema peritumoral de gliomas y metastasis cerebrales unicas, funded by Instituto de Investigacion Sanitaria H. Universitario y Politecnico La Fe. This work was partially supported by the Instituto de Aplicaciones de las Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA). Veratech for Health S.L. provided support in the form of salaries for author EF-G, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author is articulated in the "author contributions" section. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials. en_EN
dc.language Inglés es_ES
dc.publisher Public Library of Science es_ES
dc.relation info:eu-repo/grantAgreement/ESF/PTQ-1205693/EU es_ES
dc.relation.ispartof PLoS ONE es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Magnetic Resonance Imaging es_ES
dc.subject Unsupervised Classification es_ES
dc.subject Structured Prediction es_ES
dc.subject Imaging techniques es_ES
dc.subject Statistical Distributions es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1371/journal.pone.0125143
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RD12%2F0036%2F0020/ES/Cáncer/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2013-43457-R/ES/CARACTERIZACION DE FIRMAS BIOLOGICAS DE GLIOBLASTOMAS MEDIANTE MODELOS NO-SUPERVISADOS DE PREDICCION ESTRUCTURADA BASADOS EN BIOMARCADORES DE IMAGEN/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//IISLaFe%2FCON2014001/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//IISLaFe%2FCON2014002/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Juan Albarracín, J.; Fuster García, E.; Manjón Herrera, JV.; Robles Viejo, M.; Aparici, F.; Marti-Bonmati, L.; García Gómez, JM. (2015). Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification. PLoS ONE. 10(5):1-20. https://doi.org/10.1371/journal.pone.0125143 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1371/journal.pone.0125143 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 20 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 5 es_ES
dc.relation.senia 285758 es_ES
dc.identifier.pmid 25978453 en_EN
dc.identifier.pmcid PMC4433123
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Ministerio de Educación y Ciencia es_ES
dc.contributor.funder Institute of Information and Communication Technologies es_ES
dc.contributor.funder European Social Fund
dc.description.references Wen, P. Y., Macdonald, D. R., Reardon, D. A., Cloughesy, T. F., Sorensen, A. G., Galanis, E., … Chang, S. M. (2010). Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. Journal of Clinical Oncology, 28(11), 1963-1972. doi:10.1200/jco.2009.26.3541 es_ES
dc.description.references Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Physics in Medicine and Biology, 58(13), R97-R129. doi:10.1088/0031-9155/58/13/r97 es_ES
dc.description.references Dolecek, T. A., Propp, J. M., Stroup, N. E., & Kruchko, C. (2012). CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2005-2009. Neuro-Oncology, 14(suppl 5), v1-v49. doi:10.1093/neuonc/nos218 es_ES
dc.description.references Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426-1438. doi:10.1016/j.mri.2013.05.002 es_ES
dc.description.references Verma, R., Zacharaki, E. I., Ou, Y., Cai, H., Chawla, S., Lee, S.-K., … Davatzikos, C. (2008). Multiparametric Tissue Characterization of Brain Neoplasms and Their Recurrence Using Pattern Classification of MR Images. Academic Radiology, 15(8), 966-977. doi:10.1016/j.acra.2008.01.029 es_ES
dc.description.references Jensen, T. R., & Schmainda, K. M. (2009). Computer-aided detection of brain tumor invasion using multiparametric MRI. Journal of Magnetic Resonance Imaging, 30(3), 481-489. doi:10.1002/jmri.21878 es_ES
dc.description.references Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324 es_ES
dc.description.references Wagstaff KL. Intelligent Clustering with instance-level constraints. PhD Thesis, Cornell University. 2002. es_ES
dc.description.references Fletcher-Heath, L. M., Hall, L. O., Goldgof, D. B., & Murtagh, F. R. (2001). Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artificial Intelligence in Medicine, 21(1-3), 43-63. doi:10.1016/s0933-3657(00)00073-7 es_ES
dc.description.references Nie, J., Xue, Z., Liu, T., Young, G. S., Setayesh, K., Guo, L., & Wong, S. T. C. (2009). Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field. Computerized Medical Imaging and Graphics, 33(6), 431-441. doi:10.1016/j.compmedimag.2009.04.006 es_ES
dc.description.references Zhu, Y., Young, G. S., Xue, Z., Huang, R. Y., You, H., Setayesh, K., … Wong, S. T. (2012). Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation. Academic Radiology, 19(8), 977-985. doi:10.1016/j.acra.2012.03.026 es_ES
dc.description.references Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45-57. doi:10.1109/42.906424 es_ES
dc.description.references Vijayakumar, C., Damayanti, G., Pant, R., & Sreedhar, C. M. (2007). Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps. Computerized Medical Imaging and Graphics, 31(7), 473-484. doi:10.1016/j.compmedimag.2007.04.004 es_ES
dc.description.references Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., & Gerig, G. (2003). Automatic brain tumor segmentation by subject specific modification of atlas priors1. Academic Radiology, 10(12), 1341-1348. doi:10.1016/s1076-6332(03)00506-3 es_ES
dc.description.references Gudbjartsson, H., & Patz, S. (1995). The rician distribution of noisy mri data. Magnetic Resonance in Medicine, 34(6), 910-914. doi:10.1002/mrm.1910340618 es_ES
dc.description.references Buades, A., Coll, B., & Morel, J. M. (2005). A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling & Simulation, 4(2), 490-530. doi:10.1137/040616024 es_ES
dc.description.references Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003 es_ES
dc.description.references Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87-97. doi:10.1109/42.668698 es_ES
dc.description.references Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320. doi:10.1109/tmi.2010.2046908 es_ES
dc.description.references Manjón, J. V., Coupé, P., Buades, A., Collins, D. L., & Robles, M. (2010). MRI Superresolution Using Self-Similarity and Image Priors. International Journal of Biomedical Imaging, 2010, 1-11. doi:10.1155/2010/425891 es_ES
dc.description.references Rousseau, F. (2010). A non-local approach for image super-resolution using intermodality priors☆. Medical Image Analysis, 14(4), 594-605. doi:10.1016/j.media.2010.04.005 es_ES
dc.description.references Protter, M., Elad, M., Takeda, H., & Milanfar, P. (2009). Generalizing the Nonlocal-Means to Super-Resolution Reconstruction. IEEE Transactions on Image Processing, 18(1), 36-51. doi:10.1109/tip.2008.2008067 es_ES
dc.description.references Manjón, J. V., Coupé, P., Buades, A., Fonov, V., Louis Collins, D., & Robles, M. (2010). Non-local MRI upsampling. Medical Image Analysis, 14(6), 784-792. doi:10.1016/j.media.2010.05.010 es_ES
dc.description.references Kassner, A., & Thornhill, R. E. (2010). Texture Analysis: A Review of Neurologic MR Imaging Applications. American Journal of Neuroradiology, 31(5), 809-816. doi:10.3174/ajnr.a2061 es_ES
dc.description.references Ahmed, S., Iftekharuddin, K. M., & Vossough, A. (2011). Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI. IEEE Transactions on Information Technology in Biomedicine, 15(2), 206-213. doi:10.1109/titb.2011.2104376 es_ES
dc.description.references Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. doi:10.1109/tit.1982.1056489 es_ES
dc.description.references Dunn, J. C. (1973). A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3(3), 32-57. doi:10.1080/01969727308546046 es_ES
dc.description.references Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. doi:10.1007/978-1-4757-0450-1 es_ES
dc.description.references Hammersley, JM, Clifford, P. Markov fields on finite graphs and lattices. 1971. es_ES
dc.description.references Komodakis, N., & Tziritas, G. (2007). Approximate Labeling via Graph Cuts Based on Linear Programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(8), 1436-1453. doi:10.1109/tpami.2007.1061 es_ES
dc.description.references Komodakis, N., Tziritas, G., & Paragios, N. (2008). Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies. Computer Vision and Image Understanding, 112(1), 14-29. doi:10.1016/j.cviu.2008.06.007 es_ES
dc.description.references Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54(1), 313-327. doi:10.1016/j.neuroimage.2010.07.033 es_ES
dc.description.references Fonov, V., Evans, A., McKinstry, R., Almli, C., & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102. doi:10.1016/s1053-8119(09)70884-5 es_ES
dc.description.references Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., … Parsey, R. V. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3), 786-802. doi:10.1016/j.neuroimage.2008.12.037 es_ES
dc.description.references AVANTS, B., EPSTEIN, C., GROSSMAN, M., & GEE, J. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26-41. doi:10.1016/j.media.2007.06.004 es_ES
dc.description.references Saez C, Robles M, Garcia-Gomez JM. Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research 2014; In press. es_ES


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