Automatic nuclei segmentation in H&E stained breast cancer histopathology images.

The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mitko Veta, Paul J van Diest, Robert Kornegoor, André Huisman, Max A Viergever, Josien P W Pluim
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2423be039e0648209312809e05f0eb61
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2423be039e0648209312809e05f0eb61
record_format	dspace
spelling	oai:doaj.org-article:2423be039e0648209312809e05f0eb612021-11-18T09:02:13ZAutomatic nuclei segmentation in H&E stained breast cancer histopathology images.1932-620310.1371/journal.pone.0070221https://doaj.org/article/2423be039e0648209312809e05f0eb612013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23922958/?tool=EBIhttps://doaj.org/toc/1932-6203The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.Mitko VetaPaul J van DiestRobert KornegoorAndré HuismanMax A ViergeverJosien P W PluimPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e70221 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mitko Veta Paul J van Diest Robert Kornegoor André Huisman Max A Viergever Josien P W Pluim Automatic nuclei segmentation in H&E stained breast cancer histopathology images.
description	The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.
format	article
author	Mitko Veta Paul J van Diest Robert Kornegoor André Huisman Max A Viergever Josien P W Pluim
author_facet	Mitko Veta Paul J van Diest Robert Kornegoor André Huisman Max A Viergever Josien P W Pluim
author_sort	Mitko Veta
title	Automatic nuclei segmentation in H&E stained breast cancer histopathology images.
title_short	Automatic nuclei segmentation in H&E stained breast cancer histopathology images.
title_full	Automatic nuclei segmentation in H&E stained breast cancer histopathology images.
title_fullStr	Automatic nuclei segmentation in H&E stained breast cancer histopathology images.
title_full_unstemmed	Automatic nuclei segmentation in H&E stained breast cancer histopathology images.
title_sort	automatic nuclei segmentation in h&e stained breast cancer histopathology images.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/2423be039e0648209312809e05f0eb61
work_keys_str_mv	AT mitkoveta automaticnucleisegmentationinhestainedbreastcancerhistopathologyimages AT pauljvandiest automaticnucleisegmentationinhestainedbreastcancerhistopathologyimages AT robertkornegoor automaticnucleisegmentationinhestainedbreastcancerhistopathologyimages AT andrehuisman automaticnucleisegmentationinhestainedbreastcancerhistopathologyimages AT maxaviergever automaticnucleisegmentationinhestainedbreastcancerhistopathologyimages AT josienpwpluim automaticnucleisegmentationinhestainedbreastcancerhistopathologyimages
_version_	1718421012182204416

Automatic nuclei segmentation in H&E stained breast cancer histopathology images.

Ejemplares similares