A quantiative approach for the assessment of the pelvic dynamics modeling

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Uterus segmentation in dynamic MRI using LBP texture descriptors

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Multi-object segmentation framework using deformable models for medical imaging analysis

International audienceSegmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual tech- niques. This paper presents a novel open-source framework called Deformable Models Array (DMA) for the segmentation of multiple and complex structures of inter- est in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also intro- duces a control module that coordinates the cooperative evolution of the snakes, and is able to solve interaction issues towards the segmentation goal. Thus, DMA can implement complex-object and multi-object segmentation in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) show that the proposed framework has a wide range of applications especially in the presence of adjacent structures of interest or under intra-structure inhomogeneities giving excellent quantitative results

Valor das provas de posicionamento da ponta da agulha de veress em punção do hipocôndrio esquerdo na instalação do pneumoperitônio

OBJETIVO: Avaliar provas de posicionamento da agulha de Veress no hipocôndrio esquerdo na criação do pneumoperitônio. MÉTODO: Em cem pacientes puncionados no hipocôndrio esquerdo, provas de posicionamento da agulha foram avaliadas, considerando-as positivas quando, na prova da aspiração (PA), material orgânico era aspirado; na prova da resistência (PRes), exercia-se pequena força no êmbolo da seringa à infusão de líquido; na prova da recuperação (PRec), não se recobrava o líquido infundido; na do gotejamento (PG), as gotas escoavam rapidamente e, na prova da pressão intraperitoneal inicial (PPII), os níveis eram = 8mmHg. PA positiva denunciava iatrogenia, enquanto que PRes, PRec, PG e PPII positivas indicavam que a ponta da agulha estava adequadamente posicionada na cavidade peritoneal. Foram calculadas a sensibilidade (S) e a especificidade (E) das provas, e os seus valores preditivos positivos (VPP) e negativos (VPN), mediante correlação dos resultados verdadeiro-positivos (a), falso-positivos (b), falso negativos (c) e verdadeiro-negativos (d), segundo as fórmulas: S=[a/(a+c)]x100; E=[d/(b+d)]x100; VPP=[a/(a+b)]x100; VPN=[d(c+d)]x100. RESULTADOS: Na PA, constatou-se que S e VPP não puderam ser aplicados, e E=100% e VPN=100%. Na PRes, S=0%, E=100%, VPP=não existiu e VPN=90%. Tanto na PRec quanto na PG, S=50%, E=100%, VPP=100% e VPN=94,7%. Na PPII, obteve-se que S, E, VPP e VPN=100%. CONCLUSÕES: Na punção no hipocôndrio esquerdo, PA negativa garante ausência de lesões; a PRes é insegura quanto ao mau posicionamento da agulha, mas indica corretamente o bom; a PRec e a PG não reconhecem bem o adequado posicionamento, mas detectam com segurança o inadequado; a PPII acusa com segurança tanto o mau quanto o bom posicionamento da agulha, sendo a prova mais confiável dentre as estudadas