Artículo
Epithelial Ovarian Cancer Diagnosis of SecondHarmonic Generation Images: A Semiautomatic Collagen Fibers Quantification Protocol
Zeitoune, Angel Alberto
; Luna, Johana S. J.; Sanchez Salas, Kynthia; Erbes, Luciana Ariadna
; Cesar, Carlos L.; Andrade, Liliana A. L. A.; Carvahlo, Hernades F.; Bottcher Luiz, Fátima; Casco, Victor Hugo; Adur, Javier Fernando
Fecha de publicación:
02/2017
Editorial:
SAGE Publications
Revista:
Cancer Informatics
ISSN:
1176-9351
e-ISSN:
1176-9351
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
Palabras clave:
Fft
,
Glcm
,
Shg Microscopy
,
Human Cancer Ovary
,
Diagnostic Protocol
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Articulos(SEDE CENTRAL)
Articulos de SEDE CENTRAL
Articulos de SEDE CENTRAL
Citación
Zeitoune, Angel Alberto; Luna, Johana S. J.; Sanchez Salas, Kynthia; Erbes, Luciana Ariadna; Cesar, Carlos L.; et al.; Epithelial Ovarian Cancer Diagnosis of SecondHarmonic Generation Images: A Semiautomatic Collagen Fibers Quantification Protocol; SAGE Publications; Cancer Informatics; 16; 2-2017; 1-12
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