Learning deep features for dead and living breast cancer cell classification without staining

Abstract Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recen...

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Autores principales:	Gisela Pattarone, Laura Acion, Marina Simian, Emmanuel Iarussi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f0931625d1854faaa1f1cbce56932ac0
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spelling	oai:doaj.org-article:f0931625d1854faaa1f1cbce56932ac02021-12-02T15:54:50ZLearning deep features for dead and living breast cancer cell classification without staining10.1038/s41598-021-89895-w2045-2322https://doaj.org/article/f0931625d1854faaa1f1cbce56932ac02021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89895-whttps://doaj.org/toc/2045-2322Abstract Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several classifiers were trained based on well-known convolutional neural networks (CNN) backbones to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.Gisela PattaroneLaura AcionMarina SimianEmmanuel IarussiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gisela Pattarone Laura Acion Marina Simian Emmanuel Iarussi Learning deep features for dead and living breast cancer cell classification without staining
description	Abstract Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several classifiers were trained based on well-known convolutional neural networks (CNN) backbones to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.
format	article
author	Gisela Pattarone Laura Acion Marina Simian Emmanuel Iarussi
author_facet	Gisela Pattarone Laura Acion Marina Simian Emmanuel Iarussi
author_sort	Gisela Pattarone
title	Learning deep features for dead and living breast cancer cell classification without staining
title_short	Learning deep features for dead and living breast cancer cell classification without staining
title_full	Learning deep features for dead and living breast cancer cell classification without staining
title_fullStr	Learning deep features for dead and living breast cancer cell classification without staining
title_full_unstemmed	Learning deep features for dead and living breast cancer cell classification without staining
title_sort	learning deep features for dead and living breast cancer cell classification without staining
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f0931625d1854faaa1f1cbce56932ac0
work_keys_str_mv	AT giselapattarone learningdeepfeaturesfordeadandlivingbreastcancercellclassificationwithoutstaining AT lauraacion learningdeepfeaturesfordeadandlivingbreastcancercellclassificationwithoutstaining AT marinasimian learningdeepfeaturesfordeadandlivingbreastcancercellclassificationwithoutstaining AT emmanueliarussi learningdeepfeaturesfordeadandlivingbreastcancercellclassificationwithoutstaining
_version_	1718385486574125056

Learning deep features for dead and living breast cancer cell classification without staining

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