Brenda Navarro’s research while affiliated with Fundação Oswaldo Cruz and other places

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Publications (1)


Figure 1. Two images of glomeruli. (a) Glomerulus with a proliferative glomerulopathy. (b) Normal glomerulus. The enlarged areas (a' ,b') emphasize cell clusters and highlight proliferative vs non-proliferative areas, respectively. Stained with hematoxylin and eosin. Magnification bar = 60 μ m. 
Figure 3. PathoSpotter-K system architecture. 
Figure 4. Operation for separating the hematoxylin information (b) from the original colored image (a). 
Figure 5. Segmentation stages. 
Figure 6. Distribution of the images into the feature space. 

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PathoSpotter-K: A computational tool for the automatic identification of glomerular lesions in histological images of kidneys
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April 2017

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Brenda Navarro

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PathoSpotter is a computational system designed to assist pathologists in teaching about and researching kidney diseases. PathoSpotter-K is the version that was developed to detect nephrological lesions in digital images of kidneys. Here, we present the results obtained using the first version of PathoSpotter-K, which uses classical image processing and pattern recognition methods to detect proliferative glomerular lesions with an accuracy of 88.3 ± 3.6%. Such performance is only achieved by similar systems if they use images of cell in contexts that are much less complex than the glomerular structure. The results indicate that the approach can be applied to the development of systems designed to train pathology students and to assist pathologists in determining large-scale clinicopathological correlations in morphological research.

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Citations (1)


... As seções 4 e 5 apresentam os resultados parciais alcançados e, por fim, a Seção 6 discute o progresso do projeto e perspectivas futuras. Barros et al. [Barros et al. 2017] introduzem o PathoSpotter-K, um sistema derivado do projeto PathoSpotter para detectar lesões glomerulares proliferativas. Utilizando técnicas convencionais de processamento de imagens e reconhecimento de padrões, o PathoSpotter-K foi testado em um conjunto de 811 imagens, incluindo 300 imagens de glomérulos normais e 511 imagens de glomérulos de rins afetados por glomerulopatias proliferativas. ...

Reference:

Seleção de Backbone Para Extração de Características com a U-Net na Segmentação de Patologias Renais
PathoSpotter-K: A computational tool for the automatic identification of glomerular lesions in histological images of kidneys