Brain Tissue Classification to Detect Focal Cortical Dysplasia in Magnetic Resonance Imaging

  • Fabricio Simozo Universidade de São Paulo
  • Marcos Oliveira Universidade de São Paulo
  • Luiz Murta-Junior FFCLRP-USP

Resumo


Focal cortical dysplasia (FCD) is a local malformation of the cortex, the main cause of refractory epilepsy in childhood and one of the most common causes in adults. The surgery decision and planning depend on the FCD localization. Although recent studies have successfully detected FCD through artificial intelligence, no study investigates the relevance and prevalence of cortical features on FCD identification and the performance of different machine learning techniques. In this study, the proposed method constructed a voxel-based set of features, e.g., texture measure, border definition, cortical thickness.

Palavras-chave: Brain Tissue Classification, Focal Cortical Dysplasia, Magnetic Resonance Imaging

Referências

Adler, S., Wagstyl, K., Gunny, R., Ronan, L., Carmichael, D., Cross, J. H., … Baldeweg, T. (2017). Novel surface features for automated detection of focal cortical dysplasias in pediatric epilepsy. NeuroImage: Clinical, 14, 18–27. http://doi.org/10.1016/j.nicl.2016.12.030

Ahmed, B., Brodley, C. E., Blackmon, K. E., Kuzniecky, R., Barash, G., Carlson, C., … Thesen, T. (2015). Cortical feature analysis and machine learning improves detection of MRI-negative focal cortical dysplasia. Epilepsy & Behavior, 48, 21–28. http://doi.org/10.1016/j.yebeh.2015.04.055

Antel, S. B., Collins, D. L., Bernasconi, N., Andermann, F., Shinghal, R., Kearney, R. E., … Bernasconi, A. (2003). Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis. NeuroImage, 19(4), 1748–1759. http://doi.org/10.1016/S1053-8119(03)00226-X

Bergo, F., & Falcao, A. (2008). FCD segmentation using texture asymmetry of MR-T1 images of the brain. 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 424–427. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4541023

Bernasconi, A., Antel, S. B., Collins, D. L., Bernasconi, N., Olivier, A., Dubeau, F., … Arnold, D. L. (2001). Texture analysis and morphological processing of magnetic resonance imaging assist detection of focal cortical dysplasia in extra-temporal partial epilepsy. Ann Neurol, 49(6), 770–775. http://doi.org/10.1002/ana.1013

Blümcke, I., Aronica, E., Miyata, H., Sarnat, #harvey B, Thom, M., Roessler, K., … Spreafico, R. (2016). International recommendation for a comprehensive neuropathologic workup of epilepsy surgery brain tissue: A consensus Task Force report from the ILAE Commission on Diagnostic Methods. Epilepsia, 57(3), 348– 358. http://doi.org/10.1111/epi.13319

Blümcke, I., Thom, M., & Aronica, E. (2011). The clinicopathologic spectrum of focal cortical dysplasias: A consensus classification proposed by an ad hoc Task Force of the ILAE Diagnostic Methods. …, 52(1), 158–174. http://doi.org/10.1111/j.15281167.2010.02777.x.The

Cantor-Rivera, D., Khan, A. R., Goubran, M., Mirsattari, S. M., & Peters, T. M. (2015). Detection of temporal lobe epilepsy using support vector machines in multiparametric quantitative MR imaging. Computerized Medical Imaging and Graphics, 41, 14–28. http://doi.org/10.1016/j.compmedimag.2014.07.002

Carass, A., Roy, S., Jog, A., Cuzzocreo, J. L., Magrath, E., Gherman, A., … Pham, D. L. (2017). Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. NeuroImage, 148, 77–102. http://doi.org/10.1016/j.neuroimage.2016.12.064

Colombo, N., Tassi, L., Galli, C., Citterio, A., Lo Russo, G., Scialfa, G., & Spreafico, R. (2003). Focal cortical dysplasias: MR imaging, histopathologic, and clinical correlations in surgically treated patients with epilepsy. AJNR. American Journal of Neuroradiology, 24(4), 724–33. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12695213

Duncan, J. S., Winston, G. P., Koepp, M. J., Ourselin, S., John, P., Frcp, S. D., … Ourselin, P. S. (2016). Brain imaging in the assessment for epilepsy surgery. The Lancet Neurology, 15(4), 420–433. http://doi.org/10.1016/S1474-4422(15)00383-X

Fauser, S., Schulze-Bonhage, A., Honegger, J., Carmona, H., Huppertz, H.-J., Pantazis, G., … Zentner, J. (2004). Focal cortical dysplasias: surgical outcome in 67 patients in relation to histological subtypes and dual pathology. Brain : A Journal of Neurology, 127(Pt 11), 2406–18. http://doi.org/10.1093/brain/awh277

Fisher, R. S., Acevedo, C., Arzimanoglou, A., Bogacz, A., Cross, J. H., Elger, C. E., … Wiebe, S. (2014). A practical clinical definition of epilepsy. Epilepsia, 55(4), 475– 482. http://doi.org/10.1111/epi.12550

Fisher, R. S., Cross, J. H., French, J. A., Higurashi, N., Hirsch, E., Jansen, F. E., … Zuberi, S. M. (2017). Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia, 58(4), 522–530. http://doi.org/10.1111/epi.13670

Francione, S., Vigliano, P., Tassi, L., Cardinale, F., Mai, R., Lo Russo, G., & Munari, C. (2003). Surgery for drug resistant partial epilepsy in children with focal cortical dysplasia: anatomical-clinical correlations and neurophysiological data in 10 patients. Journal of Neurology, Neurosurgery, and Psychiatry, 74(11), 1493–501. http://doi.org/10.1136/jnnp.74.11.1493

Hatt, M., Laurent, B., Ouahabi, A., Fayad, H., Tan, S., Li, L., … Visvikis, D. (2018). The first MICCAI challenge on PET tumor segmentation. Medical Image Analysis, 44, 177–195. http://doi.org/10.1016/j.media.2017.12.007

Hildebrandt, M., Pieper, T., Winkler, P., Kolodziejczyk, D., Holthausen, H., Blümcke, I., & Blu, I. (2005). Neuropathological spectrum of cortical dysplasia in children with severe focal epilepsies. Acta Neuropathologica, 110(1), 1–11. http://doi.org/10.1007/s00401-005-1016-6

Hong, S. J., Kim, H., Schrader, D., Bernasconi, N., Bernhardt, B. C., & Bernasconi, A. (2014). Automated detection of cortical dysplasia type II in MRI-negative epilepsy. Neurology, 83(1), 48–55. http://doi.org/10.1212/WNL.0000000000000543

Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417–441. http://doi.org/10.1037/h0071325

Huppertz, H.-J. (2013). Morphometric MRI Analysis. In Dysphagia (Vol. 39, pp. 73– 84). http://doi.org/10.1007/174_2012_564

Kini, L. G., Gee, J. C., & Litt, B. (2016). Computational analysis in epilepsy neuroimaging: A survey of features and methods. NeuroImage: Clinical, 11(1), 515– 529. Statistical Mechanics; Mathematical Physics; Mathematical Physics. http://doi.org/10.1016/j.nicl.2016.02.013

Krsek, P., Maton, B., Korman, B., Pacheco-Jacome, E., Jayakar, P., Dunoyer, C., … Duchowny, M. (2008). Different features of histopathological subtypes of pediatric focal cortical dysplasia. Annals of Neurology, 63(6), 758–69. http://doi.org/10.1002/ana.21398

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., … Van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024. http://doi.org/10.1109/TMI.2014.2377694

Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559–572. http://doi.org/10.1080/14786440109462720

Ramli, N., Rahmat, K., Lim, K. S., & Tan, C. T. (2015). Neuroimaging in refractory epilepsy. Current practice and evolving trends. European Journal of Radiology, 84(9), 1791–1800. http://doi.org/10.1016/j.ejrad.2015.03.024

Roy, H., Lippé, S., Lussier, F., Sauerwein, H. C., Lortie, A., Lacroix, J., & Lassonde, M. (2011). Developmental outcome after a single episode of status epilepticus. Epilepsy & Behavior : E&B, 21(4), 430–6. http://doi.org/10.1016/j.yebeh.2011.05.009

Sheppard, E., & Lippé, S. (2012). Cognitive outcome of status epilepticus in children. Epilepsy Research and Treatment, 2012, 984124. http://doi.org/10.1155/2012/984124
Publicado
20/10/2020
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SIMOZO, Fabricio; OLIVEIRA, Marcos; MURTA-JUNIOR, Luiz. Brain Tissue Classification to Detect Focal Cortical Dysplasia in Magnetic Resonance Imaging. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 615-625. DOI: https://doi.org/10.5753/eniac.2020.12164.