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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2012
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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. |
format | Online Article Text |
id | pubmed-3312711 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
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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