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Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling

BACKGROUND: Segmenting cell nuclei in microscopic images has become one of the most important routines in modern biological applications. With the vast amount of data, automatic localization, i.e. detection and segmentation, of cell nuclei is highly desirable compared to time-consuming manual proces...

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Autores principales: Song, Yang, Cai, Weidong, Huang, Heng, Wang, Yue, Feng, David Dagan, Chen, Mei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3706337/
https://www.ncbi.nlm.nih.gov/pubmed/23725412
http://dx.doi.org/10.1186/1471-2105-14-173
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author Song, Yang
Cai, Weidong
Huang, Heng
Wang, Yue
Feng, David Dagan
Chen, Mei
author_facet Song, Yang
Cai, Weidong
Huang, Heng
Wang, Yue
Feng, David Dagan
Chen, Mei
author_sort Song, Yang
collection PubMed
description BACKGROUND: Segmenting cell nuclei in microscopic images has become one of the most important routines in modern biological applications. With the vast amount of data, automatic localization, i.e. detection and segmentation, of cell nuclei is highly desirable compared to time-consuming manual processes. However, automated segmentation is challenging due to large intensity inhomogeneities in the cell nuclei and the background. RESULTS: We present a new method for automated progressive localization of cell nuclei using data-adaptive models that can better handle the inhomogeneity problem. We perform localization in a three-stage approach: first identify all interest regions with contrast-enhanced salient region detection, then process the clusters to identify true cell nuclei with probability estimation via feature-distance profiles of reference regions, and finally refine the contours of detected regions with regional contrast-based graphical model. The proposed region-based progressive localization (RPL) method is evaluated on three different datasets, with the first two containing grayscale images, and the third one comprising of color images with cytoplasm in addition to cell nuclei. We demonstrate performance improvement over the state-of-the-art. For example, compared to the second best approach, on the first dataset, our method achieves 2.8 and 3.7 reduction in Hausdorff distance and false negatives; on the second dataset that has larger intensity inhomogeneity, our method achieves 5% increase in Dice coefficient and Rand index; on the third dataset, our method achieves 4% increase in object-level accuracy. CONCLUSIONS: To tackle the intensity inhomogeneities in cell nuclei and background, a region-based progressive localization method is proposed for cell nuclei localization in fluorescence microscopy images. The RPL method is demonstrated highly effective on three different public datasets, with on average 3.5% and 7% improvement of region- and contour-based segmentation performance over the state-of-the-art.
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spelling pubmed-37063372013-07-15 Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling Song, Yang Cai, Weidong Huang, Heng Wang, Yue Feng, David Dagan Chen, Mei BMC Bioinformatics Research Article BACKGROUND: Segmenting cell nuclei in microscopic images has become one of the most important routines in modern biological applications. With the vast amount of data, automatic localization, i.e. detection and segmentation, of cell nuclei is highly desirable compared to time-consuming manual processes. However, automated segmentation is challenging due to large intensity inhomogeneities in the cell nuclei and the background. RESULTS: We present a new method for automated progressive localization of cell nuclei using data-adaptive models that can better handle the inhomogeneity problem. We perform localization in a three-stage approach: first identify all interest regions with contrast-enhanced salient region detection, then process the clusters to identify true cell nuclei with probability estimation via feature-distance profiles of reference regions, and finally refine the contours of detected regions with regional contrast-based graphical model. The proposed region-based progressive localization (RPL) method is evaluated on three different datasets, with the first two containing grayscale images, and the third one comprising of color images with cytoplasm in addition to cell nuclei. We demonstrate performance improvement over the state-of-the-art. For example, compared to the second best approach, on the first dataset, our method achieves 2.8 and 3.7 reduction in Hausdorff distance and false negatives; on the second dataset that has larger intensity inhomogeneity, our method achieves 5% increase in Dice coefficient and Rand index; on the third dataset, our method achieves 4% increase in object-level accuracy. CONCLUSIONS: To tackle the intensity inhomogeneities in cell nuclei and background, a region-based progressive localization method is proposed for cell nuclei localization in fluorescence microscopy images. The RPL method is demonstrated highly effective on three different public datasets, with on average 3.5% and 7% improvement of region- and contour-based segmentation performance over the state-of-the-art. BioMed Central 2013-06-02 /pmc/articles/PMC3706337/ /pubmed/23725412 http://dx.doi.org/10.1186/1471-2105-14-173 Text en Copyright © 2013 Song et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Song, Yang
Cai, Weidong
Huang, Heng
Wang, Yue
Feng, David Dagan
Chen, Mei
Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling
title Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling
title_full Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling
title_fullStr Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling
title_full_unstemmed Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling
title_short Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling
title_sort region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3706337/
https://www.ncbi.nlm.nih.gov/pubmed/23725412
http://dx.doi.org/10.1186/1471-2105-14-173
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