Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker

Julio Oscanoa; Franco Doimi; Richard Dyer; Jhahaira Araujo; Joseph Pinto; Benjamin Castaneda

doi:10.1109/EMBC.2016.7591213

Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker

Julio Oscanoa, Franco Doimi, Richard Dyer, Jhahaira Araujo, Joseph Pinto, Benjamin Castaneda

Sección de Electricidad y Electrónica

Oncosalud, AUNA

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

14 Citas (Scopus)

Resumen

Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups: those that express the ER marker and those that do not. First, the areas that belong to the carcinoma are automatically identified. Then, the algorithm evaluates features such as size and shape to correctly segment the nuclei in these fields. Finally, the Fuzzy C-Means algorithm is used to classify the detected nuclei. The method proposed was evaluated with a set of 10 images which contained 4093 cell nuclei. The algorithm correctly identified 93.1% of the nuclei, and sensitivity and specificity of the classification were 95.7% and 93.2% respectively.

Idioma original	Inglés
Título de la publicación alojada	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	2399-2402
Número de páginas	4
ISBN (versión digital)	9781457702204
DOI	https://doi.org/10.1109/EMBC.2016.7591213
Estado	Publicada - 13 oct. 2016
Evento	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, Estados Unidos Duración: 16 ago. 2016 → 20 ago. 2016

Serie de la publicación

Nombre	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volumen	2016-October
ISSN (versión impresa)	1557-170X

Conferencia

Conferencia	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
País/Territorio	Estados Unidos
Ciudad	Orlando
Período	16/08/16 → 20/08/16

ODS de las Naciones Unidas

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10.1109/EMBC.2016.7591213

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Oscanoa, J., Doimi, F., Dyer, R., Araujo, J., Pinto, J., & Castaneda, B. (2016). Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker. En 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 2399-2402). Artículo 7591213 (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.2016.7591213

Oscanoa, Julio ; Doimi, Franco ; Dyer, Richard et al. / Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker. 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. 2399-2402 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker",

abstract = "Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups: those that express the ER marker and those that do not. First, the areas that belong to the carcinoma are automatically identified. Then, the algorithm evaluates features such as size and shape to correctly segment the nuclei in these fields. Finally, the Fuzzy C-Means algorithm is used to classify the detected nuclei. The method proposed was evaluated with a set of 10 images which contained 4093 cell nuclei. The algorithm correctly identified 93.1% of the nuclei, and sensitivity and specificity of the classification were 95.7% and 93.2% respectively.",

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Oscanoa, J, Doimi, F, Dyer, R, Araujo, J, Pinto, J & Castaneda, B 2016, Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker. En 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016., 7591213, 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. 2399-2402, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, Estados Unidos, 16/08/16. https://doi.org/10.1109/EMBC.2016.7591213

Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker. / Oscanoa, Julio; Doimi, Franco; Dyer, Richard 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. 2399-2402 7591213 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker

AU - Oscanoa, Julio

AU - Doimi, Franco

AU - Dyer, Richard

AU - Araujo, Jhahaira

AU - Pinto, Joseph

AU - Castaneda, Benjamin

PY - 2016/10/13

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N2 - Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups: those that express the ER marker and those that do not. First, the areas that belong to the carcinoma are automatically identified. Then, the algorithm evaluates features such as size and shape to correctly segment the nuclei in these fields. Finally, the Fuzzy C-Means algorithm is used to classify the detected nuclei. The method proposed was evaluated with a set of 10 images which contained 4093 cell nuclei. The algorithm correctly identified 93.1% of the nuclei, and sensitivity and specificity of the classification were 95.7% and 93.2% respectively.

AB - Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups: those that express the ER marker and those that do not. First, the areas that belong to the carcinoma are automatically identified. Then, the algorithm evaluates features such as size and shape to correctly segment the nuclei in these fields. Finally, the Fuzzy C-Means algorithm is used to classify the detected nuclei. The method proposed was evaluated with a set of 10 images which contained 4093 cell nuclei. The algorithm correctly identified 93.1% of the nuclei, and sensitivity and specificity of the classification were 95.7% and 93.2% respectively.

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Oscanoa J, Doimi F, Dyer R, Araujo J, Pinto J, Castaneda B. Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker. En 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. 2399-2402. 7591213. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2016.7591213

Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker

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