Assessment of algorithms for mitosis detection in breast cancer histopathology images

Mitko Veta; Paul J. van Diest; Stefan M. Willems; Haibo Wang; Anant Madabhushi; Angel Cruz-Roa; Fabio Gonzalez; Anders B.L. Larsen; Jacob S. Vestergaard; Anders B. Dahl; Dan C. Cireşan; Jürgen Schmidhuber; Alessandro Giusti; Luca M. Gambardella; F. Boray Tek; Thomas Walter; Ching Wei Wang; Satoshi Kondo; Bogdan J. Matuszewski; Frederic Precioso; Violet Snell; Josef Kittler; Teofilo E. de Campos; Adnan M. Khan; Nasir M. Rajpoot; Evdokia Arkoumani; Miangela M. Lacle; Max A. Viergever; Josien P.W. Pluim

doi:10.1016/j.media.2014.11.010

Assessment of algorithms for mitosis detection in breast cancer histopathology images

Mitko Veta^*, Paul J. van Diest, Stefan M. Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio Gonzalez, Anders B.L. Larsen, Jacob S. Vestergaard, Anders B. Dahl, Dan C. Cireşan, Jürgen Schmidhuber, Alessandro Giusti, Luca M. Gambardella, F. Boray Tek, Thomas Walter, Ching Wei Wang, Satoshi Kondo, Bogdan J. Matuszewski, Frederic PreciosoViolet Snell, Josef Kittler, Teofilo E. de Campos, Adnan M. Khan, Nasir M. Rajpoot, Evdokia Arkoumani, Miangela M. Lacle, Max A. Viergever, Josien P.W. Pluim

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

351 Citations (Scopus)

Abstract

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.

Original language	English
Pages (from-to)	237-248
Number of pages	12
Journal	Medical Image Analysis
Volume	20
Issue number	1
DOIs	https://doi.org/10.1016/j.media.2014.11.010
Publication status	Published - 1 Feb 2015
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2014 Elsevier B.V.

Keywords

Breast cancer
Cancer grading
Digital pathology
Mitosis detection
Whole slide imaging

UN SDGs

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

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10.1016/j.media.2014.11.010

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Veta, M., van Diest, P. J., Willems, S. M., Wang, H., Madabhushi, A., Cruz-Roa, A., Gonzalez, F., Larsen, A. B. L., Vestergaard, J. S., Dahl, A. B., Cireşan, D. C., Schmidhuber, J., Giusti, A., Gambardella, L. M., Tek, F. B., Walter, T., Wang, C. W., Kondo, S., Matuszewski, B. J., ... Pluim, J. P. W. (2015). Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Analysis, 20(1), 237-248. https://doi.org/10.1016/j.media.2014.11.010

@article{761b56492f054a678cde9536b1aec296,

title = "Assessment of algorithms for mitosis detection in breast cancer histopathology images",

abstract = "The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.",

keywords = "Breast cancer, Cancer grading, Digital pathology, Mitosis detection, Whole slide imaging",

author = "Mitko Veta and {van Diest}, {Paul J.} and Willems, {Stefan M.} and Haibo Wang and Anant Madabhushi and Angel Cruz-Roa and Fabio Gonzalez and Larsen, {Anders B.L.} and Vestergaard, {Jacob S.} and Dahl, {Anders B.} and Cire{\c s}an, {Dan C.} and J{\"u}rgen Schmidhuber and Alessandro Giusti and Gambardella, {Luca M.} and Tek, {F. Boray} and Thomas Walter and Wang, {Ching Wei} and Satoshi Kondo and Matuszewski, {Bogdan J.} and Frederic Precioso and Violet Snell and Josef Kittler and {de Campos}, {Teofilo E.} and Khan, {Adnan M.} and Rajpoot, {Nasir M.} and Evdokia Arkoumani and Lacle, {Miangela M.} and Viergever, {Max A.} and Pluim, {Josien P.W.}",

note = "Publisher Copyright: {\textcopyright} 2014 Elsevier B.V.",

year = "2015",

month = feb,

day = "1",

doi = "10.1016/j.media.2014.11.010",

language = "English",

volume = "20",

pages = "237--248",

journal = "Medical Image Analysis",

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Veta, M, van Diest, PJ, Willems, SM, Wang, H, Madabhushi, A, Cruz-Roa, A, Gonzalez, F, Larsen, ABL, Vestergaard, JS, Dahl, AB, Cireşan, DC, Schmidhuber, J, Giusti, A, Gambardella, LM, Tek, FB, Walter, T, Wang, CW, Kondo, S, Matuszewski, BJ, Precioso, F, Snell, V, Kittler, J, de Campos, TE, Khan, AM, Rajpoot, NM, Arkoumani, E, Lacle, MM, Viergever, MA & Pluim, JPW 2015, 'Assessment of algorithms for mitosis detection in breast cancer histopathology images', Medical Image Analysis, vol. 20, no. 1, pp. 237-248. https://doi.org/10.1016/j.media.2014.11.010

TY - JOUR

T1 - Assessment of algorithms for mitosis detection in breast cancer histopathology images

AU - Veta, Mitko

AU - van Diest, Paul J.

AU - Willems, Stefan M.

AU - Wang, Haibo

AU - Madabhushi, Anant

AU - Cruz-Roa, Angel

AU - Gonzalez, Fabio

AU - Larsen, Anders B.L.

AU - Vestergaard, Jacob S.

AU - Dahl, Anders B.

AU - Cireşan, Dan C.

AU - Schmidhuber, Jürgen

AU - Giusti, Alessandro

AU - Gambardella, Luca M.

AU - Tek, F. Boray

AU - Walter, Thomas

AU - Wang, Ching Wei

AU - Kondo, Satoshi

AU - Matuszewski, Bogdan J.

AU - Precioso, Frederic

AU - Snell, Violet

AU - Kittler, Josef

AU - de Campos, Teofilo E.

AU - Khan, Adnan M.

AU - Rajpoot, Nasir M.

AU - Arkoumani, Evdokia

AU - Lacle, Miangela M.

AU - Viergever, Max A.

AU - Pluim, Josien P.W.

PY - 2015/2/1

Y1 - 2015/2/1

N2 - The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.

AB - The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.

KW - Breast cancer

KW - Cancer grading

KW - Digital pathology

KW - Mitosis detection

KW - Whole slide imaging

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U2 - 10.1016/j.media.2014.11.010

DO - 10.1016/j.media.2014.11.010

M3 - Article

C2 - 25547073

AN - SCOPUS:84920921065

SN - 1361-8415

VL - 20

SP - 237

EP - 248

JO - Medical Image Analysis

JF - Medical Image Analysis

IS - 1

ER -

Assessment of algorithms for mitosis detection in breast cancer histopathology images

Abstract

Bibliographical note

Keywords

UN SDGs

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