Automatic classification of cancer cells in multispectral microscopic images of lymph node samples

Gali Zimmerman-Moreno; Irina Marin; Moshe Lindner; Iris Barshack; Yuval Garini; Eli Konen

doi:10.1109/EMBC.2016.7591597

Automatic classification of cancer cells in multispectral microscopic images of lymph node samples

Gali Zimmerman-Moreno, Irina Marin, Moshe Lindner, Iris Barshack, Yuval Garini, Eli Konen

Tel Aviv University, Bar-Ilan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81%-100% for the cancer class.

Original language	English
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3973-3976
Number of pages	4
ISBN (Electronic)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7591597
State	Published - 13 Oct 2016
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: 16 Aug 2016 → 20 Aug 2016

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2016-October

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/Territory	United States
City	Orlando
Period	16/08/16 → 20/08/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2016.7591597

Cite this

Zimmerman-Moreno, G., Marin, I., Lindner, M., Barshack, I., Garini, Y., & Konen, E. (2016). Automatic classification of cancer cells in multispectral microscopic images of lymph node samples. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 3973-3976). Article 7591597 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7591597

Zimmerman-Moreno, Gali ; Marin, Irina ; Lindner, Moshe et al. / Automatic classification of cancer cells in multispectral microscopic images of lymph node samples. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 3973-3976 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Automatic classification of cancer cells in multispectral microscopic images of lymph node samples",

abstract = "Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81\%-100\% for the cancer class.",

author = "Gali Zimmerman-Moreno and Irina Marin and Moshe Lindner and Iris Barshack and Yuval Garini and Eli Konen",

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doi = "10.1109/EMBC.2016.7591597",

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Zimmerman-Moreno, G, Marin, I, Lindner, M, Barshack, I, Garini, Y & Konen, E 2016, Automatic classification of cancer cells in multispectral microscopic images of lymph node samples. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016., 7591597, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2016-October, Institute of Electrical and Electronics Engineers Inc., pp. 3973-3976, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 16/08/16. https://doi.org/10.1109/EMBC.2016.7591597

Automatic classification of cancer cells in multispectral microscopic images of lymph node samples. / Zimmerman-Moreno, Gali; Marin, Irina; Lindner, Moshe et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 3973-3976 7591597 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic classification of cancer cells in multispectral microscopic images of lymph node samples

AU - Zimmerman-Moreno, Gali

AU - Marin, Irina

AU - Lindner, Moshe

AU - Barshack, Iris

AU - Garini, Yuval

AU - Konen, Eli

PY - 2016/10/13

Y1 - 2016/10/13

N2 - Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81%-100% for the cancer class.

AB - Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81%-100% for the cancer class.

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M3 - منشور من مؤتمر

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Y2 - 16 August 2016 through 20 August 2016

ER -

Zimmerman-Moreno G, Marin I, Lindner M, Barshack I, Garini Y, Konen E. Automatic classification of cancer cells in multispectral microscopic images of lymph node samples. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 3973-3976. 7591597. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2016.7591597

Automatic classification of cancer cells in multispectral microscopic images of lymph node samples

Abstract

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Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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