Framework para a análise da microestrutura do corpo caloso ao longo de sua extensão

Orientador: Roberto de Alencar LotufoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O corpo caloso é de grande interesse para a comunidade médica e de pesquisa, e suas características têm sido associadas a muitos distúrbios psicológicos e doenças cerebrais. A análise localizada de suas características é um procedimento usual, particularmente para o diagnóstico de esclerose múltipla e outras doenças inflamatórias. Neste trabalho, propomos um framework para extrair características da microestrutura ao longo da extensão do corpo caloso em uma função de assinatura, permitindo que análises globais e localizadas sejam realizadas no domínio 1--D da assinatura, em vez do domínio 3--D de a imagem original. Nossa solução é uma sucessão de vários métodos especializados, que foram projetados para resolver partes específicas do pipeline de geração de assinatura, incluindo a definição de um plano de simetria local para as fibras internas do corpo caloso, realizar a segmentação do corpo caloso, traçar o eixo médio da estrutura e extrair as características ao longo do eixo médio. Um dataset com imagens de 80 aquisições distintas de indivíduos saudáveis foi usado para avaliar tanto o plano de simetria da fibra quanto as assinaturas geradas. Os resultados mostram que o plano predito pelo nosso método é significativamente distinto dos planos preditos pelos métodos tradicionais de estimativa do plano sagital médio, com uma diferença maior na inclinação em relação ao plano axial, de cerca de 2 graus em média. As assinaturas apresentam um padrão similar na maioria dos casos, mas retêm características individuais. Em uma análise de agrupamento, verificamos que existe um único aglomerado maior que tem seu tamanho reduzido drasticamente apenas quando todas as arestas são removidas, exceto aquelas com pelo menos 90% de similaridade. As assinaturas geradas pelo nosso framework proposto fornecem uma maneira inédita de realizar a análise das características da microestrutura do corpo caloso, que é inerentemente localizada e independente da morfologia da estrutura. Nossa solução abre novas possibilidades no campo para pesquisa e desenvolvimento futuros relacionadosAbstract: The corpus callosum is of great interest for the medical and research community, and its characteristics have been associated with many psychological disorders and brain diseases. Localized analysis of its features is a usual procedure, particularly for the diagnosis of multiple sclerosis and other inflammatory diseases. In this work, we propose a framework for extracting microstructure features along the corpus callosum extent into a signature function, allowing global and localized analyses to be performed in the 1--D domain of the signature, instead of the 3--D domain of the original image. Our solution is a succession of several specialized methods, which were designed to solve specific parts of the signature generation pipeline, including defining a plane of local symmetry for the corpus callosum internal fibers, perform the corpus callosum segmentation, trace the structure median axis, and extract the features along the median axis. A dataset with images from 80 distinct acquisitions from healthy subjects was used to evaluate both, the fiber's symmetry plane, and the generated signatures. Results show that the plane predicted by our method is significantly distinct from the planes predicted by traditional mid--sagittal plane estimation methods, with a larger difference on the inclination relative to the axial plane, of about 2 degrees on average. The signatures present a similar pattern in most cases but retain individual characteristics. In a clustering analysis, we verified that there is one single larger cluster that has its size reduced dramatically only when all edges are removed, except for the ones with at least 90% of similarity. The signatures generated by our proposed framework provide an unprecedented way to perform the analysis of the corpus callosum microstructure features, which is inherently localized, and independent from the structure morphology. Our solution open new possibilities for future related research and development in the fieldDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétrica2012/23059-8FAPES

2D Segmentation evaluation method of the corpus callosum in diffusion MRI using corpus callosum signature

Orientador: Leticia RittnerDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O corpo caloso (CC) é a maior estrutura de substância branca no cérebro, está localizada sob o córtex cerebral e conecta os dois hemisférios cerebrais, servindo de ponte de comunicação entre eles. É, portanto, uma estrutura de grande interesse no âmbito médico e de pesquisa. Sua forma e tamanho estão associadas com algumas características do sujeito e alterações na sua estrutura apresentam correlação com várias doenças e condições médicas. Nas imagens de difusão por ressonância magnética, a segmentação desta estrutura é importante já que a informação contida neste tipo de imagem permite estudar a microestrutura das fibras neuronais e os tecidos usando o modelo de difusão da água. Na literatura existem poucos métodos de segmentação do CC baseados em imagens de difusão por ressonância magnética e não existem estudos sobre avaliação quantitativa de segmentações neste espaço. A avaliação de segmentações em difusão é feita normalmente usando um padrão-ouro obtido manualmente sobre imagens de ressonância ponderadas em T1 e registrado no espaço de difusão. Porém, o registro é computacionalmente custoso e introduz erros no padrão final. Outros padrões podem ser construídos diretamente no espaço de difusão, porém também apresentam desvantagens. A avaliação quantitativa, neste caso, é feita usando uma métrica de sobreposição. Com o propósito de melhorar o esquema de avaliação usual por sobreposição, neste trabalho é proposto um método de avaliação que permite usar diretamente o padrão obtido manualmente em T1 sem ser necessário realizar o registro para o espaço de difusão. Este método está baseado no perfil de curvatura, um descritor que permite comparar segmentações através da forma, sem necessidade de sobreposição ou registro de imagens. O método proposto foi usado para avaliar segmentações em difusão obtidas através de três métodos distintos, em 145 sujeitos. A raiz do erro médio quadrático (RMSE), calculado a partir da comparação entre os perfis de curvatura, mostrou-se uma métrica complementaria ao coeficiente Dice e apresentou capacidade para discriminar segmentações. Para exploração em trabalhos futuros, o perfil de curvatura pode ser usado para identificação automática de segmentações incorretas em grandes bases de dados, estudos populacionais e longitudinais e caracterização de outras formas e estruturasAbstract: Corpus callosum (CC) is the greatest white matter structure in brain. It is located beneath the cortex and connects both of two hemispheres, making possible their communication. Therefore, CC is important in medical and academic scene. CC's shape and size are associated with some subject's characteristics and alterations in its structure have correlation with some diseases and medical conditions. Diffusion MRI makes possible study of the neuronal fibers and tissues micro structure using the water diffusion model. In the literature, there are few CC segmentation methods based-on Diffusion MRI and there are not studies related with segmentation quantitative evaluation in this model. Segmentation evaluation in diffusion is commonly performed using registered gold-standard or any standard draw directly in this modality. However, both of the two standards have problems because of the registering process itself, that introduce error in the final standard, or the drawing process on the diffusion images. Quantitative evaluation is done using overlap metric. In this work, an evaluation method is proposed making possible direct use of gold-standard without any register process. This methodology is based on CC signature, a descriptor for comparing segmentations using shape, without overlap requirement with standard. The segmentation evaluation method proposed was used in diffusion for quantitative assessment of 145 subjects using the gold-standard through CC signature. The RMSE metric, based on CC signature, showed to be complementary with Dice coefficient and capability for differentiating segmentations. For future work, the CC signature, used as characterization tool of the CC shape, would make possible automatic identification of incorrect segmentations in large databases, longitudinal studies, classification of populations and characterization of other structuresMestradoEngenharia de ComputaçãoMestre em Engenharia Elétrica190557/2014-1CNP

Watershed-based Segmentation Of The Midsagittal Section Of The Corpus Callosum In Diffusion Mri

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The corpus callosum (CC) is one of the most important white matter structures of the brain, interconnecting the two cerebral hemispheres. The corpus callosum is related to several neurodegenerative diseases and, as segmentation is usually the first step for studies in this structure, it is important to have a robust method for CC segmentation. We propose here a new approach for fully automatic segmentation of the CC in the magnetic resonance diffusion tensor images. The method uses the watershed transform and is performed on the fractional anisotropy (FA) map weighted by the projection of the principal eigenvector in the left-right direction. It first computes the section of the CC in the midsagittal slice and uses it as a seed for the 3D volume segmentation. Experiments with real diffusion MRI data showed that the proposed method is able to quickly segment the CC without any user intervention, with great results when compared to manual segmentation. Since it is simple, fast and does not require parameter settings, the proposed method is well suited for clinical applications. © 2011 IEEE.274280Cons. Nac. Desenvolv. Cient. Tecnol. (CNPq),Coordenacao Aperfeicoamento Pessoal Nivel Superior (CAPES),Fundacao de Amparo a Pesquisa do Estado de Alagoas (FAPEAL),Secr. Estado Cienc., Tecnol. Inovacao (SECTI-AL)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Hofer, S., Frahm, J., Topography of the human corpus callosum revisited-Comprehensive fiber tractography using diffusion tensor magnetic resonance imaging (2006) NeuroImage, 32 (3), pp. 989-994. , DOI 10.1016/j.neuroimage.2006.05.044, PII S1053811906006501Leys, D., Pruvo, J.-P., Could Wallerian degeneration contribute to "leuko-araiosis" in subjects free of any vascular disorder? (1991) Journal of Neurology, Neurosurgery, and Psychiatry., 54 (1), pp. 46-50Yamauchi, H., Fukuyama, H., Harada, K., Nabatame, H., Ogawa, M., Ouchi, Y., Kimura, J., Konishi, J., Callosal atrophy parallels decreased cortical oxygen metabolism and neuropsychological impairment in Alzheimer's disease (1993) Archives of Neurology, 50 (10), pp. 1070-1074Noha, E.-Z., Casanova, M., Elmaghraby, A., Variability of the relative corpus callosum cross sectional area between dyslexic and normally developed brains (2008) Proceedings of 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 436-439. , 2008Lee, S.-P., Cheng, J.-Z., Chen, C.-M., Tseng, W.-Y.I., An automatic segmentation approach for boundary delineation of corpus callosum based on cell competition (2008) Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5514 (7), p. 2008Seixas, F.L., De Souza, A.S., Augusto, A., Dos Santos, S.M.D., Saade, D.C.M., Automated segmentation of the corpus callosum midsagittal surface area (2007) Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing, pp. 287-293. , 2007Vilanova, A., Zhang, S., Kindlmann, G., An introduction to visualization of diffusion tensor imaging and its applications (2005) Visualization and Processing of Tensor Fields, p. 121153Chao, Y.-P., Cho, K.-H., Yeh, C.-H., Probabilistic topography of human corpus callosum using cytoarchitectural parcellation and high angular resolution diffusion imaging tractography (2009) Human Brain Mapping, 30 (10), pp. 3172-3187Esmaeil, D.-B., Hamid, S.-Z., Atlas based segmentation of white matter fiber bundles in DTMRI using fractional anisotropy and principal eigen vectors (2008) Proceedings of 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 879-882. , 2008Lenglet, C., Rousson, M., Deriche, R., Segmentation of 3D probability density fields by surface evolution: Application to diffusion MRI (2004) Lecture Notes in Computer Science, 3216 (PART 1), pp. 18-25. , Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, ProceedingsLenglet, C., Rousson, M., Deriche, R., Faugeras, O., Statistics on the manifold of multivariate normal distributions: Theory and application to diffusion tensor MRI processing (2006) Journal of Mathematical Imaging and Vision, 25 (3), pp. 423-444. , DOI 10.1007/s10851-006-6897-z, Special Issue on Mathematics and Image Analysis (MIA'04)Niogi, S.N., Mukherjee, P., McCandliss, B.D., Diffusion tensor imaging segmentation of white matter structures using a Reproducible Objective Quantification Scheme (ROQS) (2007) NeuroImage, 35 (1), pp. 166-174. , DOI 10.1016/j.neuroimage.2006.10.040, PII S1053811906010779Lotufo, R.A., MacHado, R.C., Körbes, A., Ramos, R.G., Adessowiki: On-line collaborative scientific programming platform (2009) Proceedings of the 5th International Symposium on Wikis and Open Collaboration, 10, pp. 1-6Beucher, S., Meyer, F., The morphological approach to segmentation: The watershed transformation (1992) Mathematical Morphology in Image Processing, pp. 433-481. , ch.12Meyer, F., An overview of morphological segmentation (2001) International Journal of Pattern Recognition and Artificial Intelligence, 15 (7), pp. 1089-1118. , DOI 10.1142/S0218001401001337, Combinatorial Image AnalysisRivest, J.-F., Soille, P., Beucher, S., Morphological gradients (1993) Journal of Electronic Imaging, 2 (4), pp. 326-33

Segmentation Of Brain Structures By Watershed Transform On Tensorial Morphological Gradient Of Diffusion Tensor Imaging

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Watershed transform on tensorial morphological gradient (TMG) is a new approach to segment diffusion tensor images (DTI). Since the TMG is able to express the tensorial dissimilarities in a single scalar image, the segmentation problem of DTI is then reduced to a scalar image segmentation problem. Therefore, it can be addressed by well-known segmentation techniques, such as the watershed transform. In other words, by computing the TMG of a DTI, and then using the hierarchical watershed transform, it is possible to segment brain structures, such as the corpus callosum, the ventricles and the cortico-spinal tracts, and use the results for subsequent quantitative analysis of DTI parameters. Experiments showed that segmentations obtained with the proposed approach are similar to the ones obtained by other segmentation techniques based on DTI and also segmentation methods based on other Magnetic Resonance Imaging (MRI) modalities. Since the proposed method, as opposed to the majority of the DTI based segmentation methods, does not require manual seed and/or surface placement, its results are highly repeatable. 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