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    Automated ensemble segmentation of epithelial proliferation, necrosis, and fibrosis using scatter tumor imaging

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    Identificadores
    URI: http://hdl.handle.net/10902/2525
    DOI: 10.1117/12.854559
    ISSN: 1996-756X
    ISSN: 0277-786X
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    Author
    García Allende, Pilar Beatriz; Conde Portilla, Olga MaríaAutoridad Unican; Krishnaswamy, Venkataramanan; Hoopes, P. Jack; Pogue, Brian William; Mirapeix Serrano, Jesús MaríaAutoridad Unican; López Higuera, José MiguelAutoridad Unican
    Date
    2010-05-17
    Derechos
    © 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
    Publicado en
    Proceedings of SPIE, 2010, vol. 7715, 77151B
    Biophotonics: Photonic Solutions for Better Health Care II, Bruselas, 2010
    Publisher
    SPIE Society of Photo-Optical Instrumentation Engineers
    Enlace a la publicación
    http://dx.doi.org/10.1117/12.854559
    Palabras clave
    Automatic classification
    Tumor
    Necrosis
    Confocal reflectance imaging
    Scatter
    Feature extraction
    K-nearest neighbors (kNN)
    Artificial neural networks (ANN)
    Abstract:
    Conventional imaging systems used today in surgical settings rely on contrast enhancement based on color and intensity and they are not sensitive to morphology changes at the microscopic level. Elastic light scattering spectroscopy has been shown to distinguish ultra-structural changes in tissue. Therefore, it could provide this intrinsic contrast being enormously useful in guiding complex surgical interventions. Scatter parameters associated with epithelial proliferation, necrosis and fibrosis in pancreatic tumors were previously estimated in a quantitative manner. Subtle variations were encountered across the distinct diagnostic categories. This work proposes an automated methodology to correlate these variations with their corresponding tumor morphologies. A new approach based on the aggregation of the predictions of K-nearest neighbors (kNN) algorithm and Artificial Neural Networks (ANNs) has been developed. The major benefit obtained from the combination of the distinct classifiers is a significant increase in the number of pixel localizations whose corresponding tissue type is reliably assured. Pseudo-color diagnosis images are provided showing a strong correlation with sample segmentations performed by a veterinary pathologist.
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    • D50 Congresos [421]
    • D50 Proyectos de Investigación [291]

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    UNIVERSIDAD DE CANTABRIA

    Repositorio realizado por la Biblioteca Universitaria utilizando DSpace software
    Contact Us | Send Feedback
    Metadatos sujetos a:licencia de Creative Commons Reconocimiento 3.0 España