Epithelial ovarian cancer diagnosis of second-harmonic generation images: a semiautomatic collagen fibers quantification protocol

A vast number of human pathologic conditions are directly or indirectly related to tissular collagen structure remodeling. The nonlinear optical microscopy second-harmonic generation has become a powerful tool for imaging biological tissues with anisotropic hyperpolarized structures, such as collage...

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Detalles Bibliográficos
Autores: Zeitoune, Angel Alberto, Luna, Johana S. J, Sanchez Salas, Kynthia, Erbes, Luciana Ariadna, Cesar, Carlos Lenz, Andrade, Liliana Aparecida Lucci de Angelo, Carvalho, Hernades Faustino de, Luiz, Fátima Aparecida Bottcher, Casco, Victor Hugo, Adur, Javier Fernando
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:Brasil
Institución:Universidade Federal do Ceará (UFC)
Repositorio:Repositório Institucional da Universidade Federal do Ceará (UFC)
Idioma:inglés
OAI Identifier:oai:repositorio.ufc.br:riufc/40808
Acceso en línea:http://www.repositorio.ufc.br/handle/riufc/40808
Access Level:acceso abierto
Palabra clave:Anisotropia
GLCM
Microscopia
Neoplasias ovarianas
Colágeno
Descripción
Sumario:A vast number of human pathologic conditions are directly or indirectly related to tissular collagen structure remodeling. The nonlinear optical microscopy second-harmonic generation has become a powerful tool for imaging biological tissues with anisotropic hyperpolarized structures, such as collagen. During the past years, several quantification methods to analyze and evaluate these images have been developed. However, automated or semiautomated solutions are necessary to ensure objectivity and reproducibility of such analysis. This work describes automation and improvement methods for calculating the anisotropy (using fast Fourier transform analysis and the gray-level co-occurrence matrix). These were applied to analyze biopsy samples of human ovarian epithelial cancer at different stages of malignancy (mucinous, serous, mixed, and endometrial subtypes). The semiautomation procedure enabled us to design a diagnostic protocol that recognizes between healthy and pathologic tissues, as well as between different tumor types.