Canine mammary cancer diagnosis from quantitative properties of nonlinear optical images
Canine mammary cancer diagnosis from quantitative properties of nonlinear optical images
We present nonlinear microscopy imaging results and analysis from canine mammary cancer biopsies. Second harmonic generation imaging allows information of the collagen structure in the extracellular matrix that together with the fluorescence of the cell regions of the biopsies form a base for comprehensive image analysis. We demonstrate an automated image analysis method to classify the histological type of canine mammary cancer using a range of parameters extracted from the images. The software developed for image processing and analysis allows for the extraction of the collagen fibre network and the cell regions of the images. Thus, the tissue properties are obtained after the segmentation of the image and the metrics are measured specifically for the collagen and the cell regions. A linear discriminant analysis including all the extracted metrics allowed to clearly separate between the healthy and cancerous tissue with a 91%-accuracy. Also, a 61%-accuracy was achieved for a comparison of healthy and three histological cancer subtypes studied.
6413-6427
Reis, Luana A.
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Garcia, Ana P. V.
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Gomes, Egleidson F. A.
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Longford, Francis G.J.
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Frey, Jeremy G.
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Cassali, Geovanni D.
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de Paula, Ana M.
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1 November 2020
Reis, Luana A.
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Garcia, Ana P. V.
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Gomes, Egleidson F. A.
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Longford, Francis G.J.
27eec433-a773-4cc7-8327-70c5103382e0
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Cassali, Geovanni D.
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de Paula, Ana M.
9868f160-10a9-443f-b179-02ca8426003e
Reis, Luana A., Garcia, Ana P. V., Gomes, Egleidson F. A., Longford, Francis G.J., Frey, Jeremy G., Cassali, Geovanni D. and de Paula, Ana M.
(2020)
Canine mammary cancer diagnosis from quantitative properties of nonlinear optical images.
Biomedical Optics Express, 11 (11), .
(doi:10.1364/BOE.400871).
Abstract
We present nonlinear microscopy imaging results and analysis from canine mammary cancer biopsies. Second harmonic generation imaging allows information of the collagen structure in the extracellular matrix that together with the fluorescence of the cell regions of the biopsies form a base for comprehensive image analysis. We demonstrate an automated image analysis method to classify the histological type of canine mammary cancer using a range of parameters extracted from the images. The software developed for image processing and analysis allows for the extraction of the collagen fibre network and the cell regions of the images. Thus, the tissue properties are obtained after the segmentation of the image and the metrics are measured specifically for the collagen and the cell regions. A linear discriminant analysis including all the extracted metrics allowed to clearly separate between the healthy and cancerous tissue with a 91%-accuracy. Also, a 61%-accuracy was achieved for a comparison of healthy and three histological cancer subtypes studied.
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boe-11-11-6413
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Accepted/In Press date: 4 October 2020
e-pub ahead of print date: 16 October 2020
Published date: 1 November 2020
Additional Information:
Funding Information:
Brazilian Institute of Science and Technology (INCT) in Carbon Nanomaterials; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; University of Southampton; Engineering and Physical Sciences Research Council (Grant No. EP/G03690X/1).
Funding Information:
We are grateful to Tauanne Dias Amarante and Leonardo F. Calazans for help with some of the statistical analysis. We thank the University of Southampton and Fapemig for the joint exchange award, FL thanks the EPSRC for the award of a Doctoral Prize fellowship funded from the University of Southampton’s EPSRC Doctoral Training Partnership grant (DTP).
Publisher Copyright:
© 2020 OSA - The Optical Society. All rights reserved.
Identifiers
Local EPrints ID: 444676
URI: http://eprints.soton.ac.uk/id/eprint/444676
ISSN: 2156-7085
PURE UUID: 5fc1c286-9f46-427d-af58-431c2c2d03df
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Date deposited: 29 Oct 2020 17:31
Last modified: 17 Mar 2024 02:33
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Contributors
Author:
Luana A. Reis
Author:
Ana P. V. Garcia
Author:
Egleidson F. A. Gomes
Author:
Francis G.J. Longford
Author:
Geovanni D. Cassali
Author:
Ana M. de Paula
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