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

Object detection and segmentation using discriminative learning

Object detection and segmentation algorithms need to use prior knowledge of objects' shape and appearance to guide solutions to correct ones. A promising way of obtaining prior knowledge is to learn it directly from expert annotations by using machine learning techniques. Previous approaches commonly use generative learning approaches to achieve this goal. In this dissertation, I propose a series of discriminative learning algorithms based on boosting principles to learn prior knowledge from image databases with expert annotations. The learned knowledge improves the performance of detection and segmentation, leading to fast and accurate solutions. For object detection, I present a learning procedure called a Probabilistic Boosting Network (PBN) suitable for real-time object detection and pose estimation. Based on the law of total probability, PBN integrates evidence from two building blocks, namely a multiclass classifier for pose estimation and a detection cascade for object detection. Both the classifier and detection cascade employ boosting. By inferring the pose parameter, I avoid the exhaustive scan over pose parameters, which hampers real-time detection. I implement PBN using a graph-structured network that alternates the two tasks of object detection and pose estimation in an effort to reject negative cases as quickly as possible. Compared with previous approaches, PBN has higher accuracy in object localization and pose estimation with noticeable reduced computation. For object segmentation, I cast deformable object segmentation as optimizing the conditional probability density function p(C|I), where I is an image and C is a vector of model parameters describing the object shape. I propose a regression approach to learn the density p(C|I) discriminatively based on boosting principles. The learned density p(C|I) possesses a desired unimodal, smooth shape, which can be used by optimization algorithms to efficiently estimate a solution. To handle the high-dimensional learning challenges, I propose a multi-level approach and a gradient-based sampling strategy to learn regression functions efficiently. I show that the regression approach consistently outperforms state-of-the-art methods on a variety of testing datasets. Finally, I present a comparative study on how to apply three discriminative learning approaches - classification, regression, and ranking - to deformable shape segmentation. I discuss how to extend the idea of the regression approach to build discriminative models using classification and ranking. I propose sampling strategies to collect training examples from a high-dimensional model space for the classification and the ranking approach. I also propose a ranking algorithm based on Rankboost to learn a discriminative model for segmentation. Experimental results on left ventricle and left atrium segmentation from ultrasound images and facial feature localization demonstrate that the discriminative models outperform generative models and energy minimization methods by a large margin