Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination
Histology is the diagnosis gold standard. Conventional biopsy presents artifacts, delays, or human bias. Digital histology includes automation and improved diagnosis. It digitalizes microscopic images of histological samples and analyzes similar parameters. The present approach proposes the novel us...
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Online Access: | https://www.mdpi.com/2076-3417/11/13/6142 |
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doaj-c57119b98e39438faf2d78e4395bbdbf2021-07-15T15:30:48ZengMDPI AGApplied Sciences2076-34172021-07-01116142614210.3390/app11136142Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues DiscriminationJosé Luis Ganoza-Quintana0Félix Fanjul-Vélez1José Luis Arce-Diego2Biomedical Engineering Group, TEISA Department, University of Cantabria, Av de los Castros 46, 39005 Santander, SpainBiomedical Engineering Group, TEISA Department, University of Cantabria, Av de los Castros 46, 39005 Santander, SpainBiomedical Engineering Group, TEISA Department, University of Cantabria, Av de los Castros 46, 39005 Santander, SpainHistology is the diagnosis gold standard. Conventional biopsy presents artifacts, delays, or human bias. Digital histology includes automation and improved diagnosis. It digitalizes microscopic images of histological samples and analyzes similar parameters. The present approach proposes the novel use of phase contrast in clinical digital histology to improve diagnosis. The use of label-free fresh tissue slices prevents processing artifacts and reduces processing time. Phase contrast parameters are implemented and calculated: the external scale, the fractal dimension, the anisotropy factor, the scattering coefficient, and the refractive index variance. Images of healthy and tumoral samples of liver, colon, and kidney are employed. A total of 252 images with 10×, 20×, and 40× magnifications are measured. Discrimination significance between healthy and tumoral tissues is assessed statistically with ANOVA (<i>p</i>-value < 0.005). The analysis is made for each tissue type and for different magnifications. It shows a dependence on tissue type and image magnification. The <i>p</i>-value of the most significant parameters is below 10<sup>−5</sup>. Liver and colon tissues present a great overlap in significant phase contrast parameters. The 10× fractal dimension is significant for all tissue types under analysis. These results are promising for the use of phase contrast in digital histology clinical praxis.https://www.mdpi.com/2076-3417/11/13/6142digital histologyphase contrast imagingbiomarkersbiomedical opticsfractal analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
José Luis Ganoza-Quintana Félix Fanjul-Vélez José Luis Arce-Diego |
spellingShingle |
José Luis Ganoza-Quintana Félix Fanjul-Vélez José Luis Arce-Diego Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination Applied Sciences digital histology phase contrast imaging biomarkers biomedical optics fractal analysis |
author_facet |
José Luis Ganoza-Quintana Félix Fanjul-Vélez José Luis Arce-Diego |
author_sort |
José Luis Ganoza-Quintana |
title |
Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination |
title_short |
Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination |
title_full |
Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination |
title_fullStr |
Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination |
title_full_unstemmed |
Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination |
title_sort |
digital histology by phase imaging specific biomarkers for human tumoral tissues discrimination |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-07-01 |
description |
Histology is the diagnosis gold standard. Conventional biopsy presents artifacts, delays, or human bias. Digital histology includes automation and improved diagnosis. It digitalizes microscopic images of histological samples and analyzes similar parameters. The present approach proposes the novel use of phase contrast in clinical digital histology to improve diagnosis. The use of label-free fresh tissue slices prevents processing artifacts and reduces processing time. Phase contrast parameters are implemented and calculated: the external scale, the fractal dimension, the anisotropy factor, the scattering coefficient, and the refractive index variance. Images of healthy and tumoral samples of liver, colon, and kidney are employed. A total of 252 images with 10×, 20×, and 40× magnifications are measured. Discrimination significance between healthy and tumoral tissues is assessed statistically with ANOVA (<i>p</i>-value < 0.005). The analysis is made for each tissue type and for different magnifications. It shows a dependence on tissue type and image magnification. The <i>p</i>-value of the most significant parameters is below 10<sup>−5</sup>. Liver and colon tissues present a great overlap in significant phase contrast parameters. The 10× fractal dimension is significant for all tissue types under analysis. These results are promising for the use of phase contrast in digital histology clinical praxis. |
topic |
digital histology phase contrast imaging biomarkers biomedical optics fractal analysis |
url |
https://www.mdpi.com/2076-3417/11/13/6142 |
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