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Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment.
Unger, Jakob; Sun, Tianchen; Chen, Yi-Ling; Phipps, Jennifer E; Bold, Richard J; Darrow, Morgan A; Ma, Kwan-Liu; Marcu, Laura.
Afiliação
  • Unger J; University of California Davis, Department of Biomedical Engineering, Davis, California, United States.
  • Sun T; University of California Davis, Department of Computer Science, Davis, California, United States.
  • Chen YL; University of California Davis, Department of Computer Science, Davis, California, United States.
  • Phipps JE; University of California Davis, Department of Biomedical Engineering, Davis, California, United States.
  • Bold RJ; University of California Davis, Department of Surgery, Sacramento, California, United States.
  • Darrow MA; University of California Davis, Department of Pathology and Laboratory Medicine, Sacramento, Califor, United States.
  • Ma KL; University of California Davis, Department of Computer Science, Davis, California, United States.
  • Marcu L; University of California Davis, Department of Biomedical Engineering, Davis, California, United States.
J Biomed Opt ; 23(1): 1-11, 2018 01.
Article em En | MEDLINE | ID: mdl-29297208
ABSTRACT
An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67 mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Neoplasias da Mama / Imagem Óptica Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Neoplasias da Mama / Imagem Óptica Idioma: En Ano de publicação: 2018 Tipo de documento: Article