System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)

Abstract This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and div...

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Autores principales:	Lukasz Roszkowiak, Anna Korzynska, Krzysztof Siemion, Jakub Zak, Dorota Pijanowska, Ramon Bosch, Marylene Lejeune, Carlos Lopez
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/48f4404a6ed94045b2b09261a81f6c5f
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spelling	oai:doaj.org-article:48f4404a6ed94045b2b09261a81f6c5f2021-12-02T16:55:53ZSystem for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)10.1038/s41598-021-88611-y2045-2322https://doaj.org/article/48f4404a6ed94045b2b09261a81f6c5f2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88611-yhttps://doaj.org/toc/2045-2322Abstract This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and diverse compactness. It stands out with the proposed seamless segmentation based on regions of interest cropping as well as the explicit step of nuclei cluster splitting followed by a boundary refinement. The system utilizes machine learning and recursive local processing to eliminate distorted (inaccurate) outlines. The method was validated using two labeled datasets which proved the relevance of the achieved results. The evaluation was based on the IISPV dataset of tissue from biopsy of breast cancer patients, with markers of T cells, along with Warwick Beta Cell Dataset of DAB&H-stained tissue from postmortem diabetes patients. Based on the comparison of the ground truth with the results of the detected and classified objects, we conclude that the proposed method can achieve better or similar results as the state-of-the-art methods. This system deals with the complex problem of nuclei quantification in digitalized images of immunohistochemically stained tissue sections, achieving best results for DAB&H-stained breast cancer tissue samples. Our method has been prepared with user-friendly graphical interface and was optimized to fully utilize the available computing power, while being accessible to users with fewer resources than needed by deep learning techniques.Lukasz RoszkowiakAnna KorzynskaKrzysztof SiemionJakub ZakDorota PijanowskaRamon BoschMarylene LejeuneCarlos LopezNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lukasz Roszkowiak Anna Korzynska Krzysztof Siemion Jakub Zak Dorota Pijanowska Ramon Bosch Marylene Lejeune Carlos Lopez System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)
description	Abstract This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and diverse compactness. It stands out with the proposed seamless segmentation based on regions of interest cropping as well as the explicit step of nuclei cluster splitting followed by a boundary refinement. The system utilizes machine learning and recursive local processing to eliminate distorted (inaccurate) outlines. The method was validated using two labeled datasets which proved the relevance of the achieved results. The evaluation was based on the IISPV dataset of tissue from biopsy of breast cancer patients, with markers of T cells, along with Warwick Beta Cell Dataset of DAB&H-stained tissue from postmortem diabetes patients. Based on the comparison of the ground truth with the results of the detected and classified objects, we conclude that the proposed method can achieve better or similar results as the state-of-the-art methods. This system deals with the complex problem of nuclei quantification in digitalized images of immunohistochemically stained tissue sections, achieving best results for DAB&H-stained breast cancer tissue samples. Our method has been prepared with user-friendly graphical interface and was optimized to fully utilize the available computing power, while being accessible to users with fewer resources than needed by deep learning techniques.
format	article
author	Lukasz Roszkowiak Anna Korzynska Krzysztof Siemion Jakub Zak Dorota Pijanowska Ramon Bosch Marylene Lejeune Carlos Lopez
author_facet	Lukasz Roszkowiak Anna Korzynska Krzysztof Siemion Jakub Zak Dorota Pijanowska Ramon Bosch Marylene Lejeune Carlos Lopez
author_sort	Lukasz Roszkowiak
title	System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)
title_short	System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)
title_full	System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)
title_fullStr	System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)
title_full_unstemmed	System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)
title_sort	system for quantitative evaluation of dab&h-stained breast cancer biopsy digital images (chisel)
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/48f4404a6ed94045b2b09261a81f6c5f
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System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)

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