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A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering

With modern automated microscopes and digital cameras, pathologists no longer have to examine samples looking through microscope binoculars. Instead, the slide is digitized to an image, which can then be examined on a screen. This creates the possibility for computers to analyze the image. In this w...

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Autores principales: Vidal, Juan, Bueno, Gloria, Galeotti, John, García-Rojo, Marcial, Relea, Fernanda, Déniz, Oscar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3312711/
https://www.ncbi.nlm.nih.gov/pubmed/22811961
http://dx.doi.org/10.4103/2153-3539.92032
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author Vidal, Juan
Bueno, Gloria
Galeotti, John
García-Rojo, Marcial
Relea, Fernanda
Déniz, Oscar
author_facet Vidal, Juan
Bueno, Gloria
Galeotti, John
García-Rojo, Marcial
Relea, Fernanda
Déniz, Oscar
author_sort Vidal, Juan
collection PubMed
description With modern automated microscopes and digital cameras, pathologists no longer have to examine samples looking through microscope binoculars. Instead, the slide is digitized to an image, which can then be examined on a screen. This creates the possibility for computers to analyze the image. In this work, a fully automated approach to region of interest (ROI) segmentation in prostate biopsy images is proposed. This will allow the pathologists to focus on the most important areas of the image. The method proposed is based on level-set and mean filtering techniques for lumen centered expansion and cell density localization respectively. The novelty of the technique lies in the ability to detect complete ROIs, where a ROI is composed by the conjunction of three different structures, that is, lumen, cytoplasm, and cells, as well as regions with a high density of cells. The method is capable of dealing with full biopsies digitized at different magnifications. In this paper, results are shown with a set of 100 H and E slides, digitized at 5×, and ranging from 12 MB to 500 MB. The tests carried out show an average specificity above 99% across the board and average sensitivities of 95% and 80%, respectively, for the lumen centered expansion and cell density localization. The algorithms were also tested with images at 10× magnification (up to 1228 MB) obtaining similar results.
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spelling pubmed-33127112012-07-18 A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering Vidal, Juan Bueno, Gloria Galeotti, John García-Rojo, Marcial Relea, Fernanda Déniz, Oscar J Pathol Inform Symposium - Original Research With modern automated microscopes and digital cameras, pathologists no longer have to examine samples looking through microscope binoculars. Instead, the slide is digitized to an image, which can then be examined on a screen. This creates the possibility for computers to analyze the image. In this work, a fully automated approach to region of interest (ROI) segmentation in prostate biopsy images is proposed. This will allow the pathologists to focus on the most important areas of the image. The method proposed is based on level-set and mean filtering techniques for lumen centered expansion and cell density localization respectively. The novelty of the technique lies in the ability to detect complete ROIs, where a ROI is composed by the conjunction of three different structures, that is, lumen, cytoplasm, and cells, as well as regions with a high density of cells. The method is capable of dealing with full biopsies digitized at different magnifications. In this paper, results are shown with a set of 100 H and E slides, digitized at 5×, and ranging from 12 MB to 500 MB. The tests carried out show an average specificity above 99% across the board and average sensitivities of 95% and 80%, respectively, for the lumen centered expansion and cell density localization. The algorithms were also tested with images at 10× magnification (up to 1228 MB) obtaining similar results. Medknow Publications & Media Pvt Ltd 2012-01-19 /pmc/articles/PMC3312711/ /pubmed/22811961 http://dx.doi.org/10.4103/2153-3539.92032 Text en Copyright: © 2011 Vidal J. http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Symposium - Original Research
Vidal, Juan
Bueno, Gloria
Galeotti, John
García-Rojo, Marcial
Relea, Fernanda
Déniz, Oscar
A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering
title A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering
title_full A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering
title_fullStr A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering
title_full_unstemmed A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering
title_short A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering
title_sort fully automated approach to prostate biopsy segmentation based on level-set and mean filtering
topic Symposium - Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3312711/
https://www.ncbi.nlm.nih.gov/pubmed/22811961
http://dx.doi.org/10.4103/2153-3539.92032
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