Corneal endothelium assessment is carried out via specular microscopy imaging. However, automated image analysis often fails due to inadequate image quality conditions or the presence of dark regions in pathologies such as Fuchs’ dystrophy. Therefore, an early reliable image classification strategy is required before automated evaluation based on cell segmentation. Moreover, conventional classification approaches rely on manually labeled data which are difficult to obtain. We propose a two-stage semi-supervised classification algorithm, feature detection and prediction of a blurring level and guttae severity that allows us to cluster images based on the degree of segmentation complexity. For validation, we developed a web-based annotation application and surveyed a pair of expert ophthalmologists for grading a portion of the 1169 images. Preliminary results show that this approach provides a reliable and fast approach for corneal endothelial cell (CEC) image classification.
El siguiente artículo presenta una metodología de aplicación de un análisis de tolerancia al daño a la estructura del ala de una aeronave categoría LSA. Este estudio contempla un análisis CFD en ANSYS Fluent donde se calculan las cargas aerodinámicas que actúan sobre el ala, seguido de un análisis de resistencia estructural en el cual se realiza una integración Fluid-Structure Interface con la que se determinan los esfuerzos máximos soportados por la estructura mediante Elementos Finitos (FEA) realizado en ANSYS Workbench, un análisis de tamaños de grieta críticos sobre los elementos primarios de la estructura y un calculo de vida residual de estos elementos con el fin de crear un intervalo de inspecciones NDT para garantizar la integridad estructural del ala de la aeronave.
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