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Assessment of algorithms for mitosis detection in breast cancer histopathology images

Veta, M, van Diest, PJ, Willems, SM, Wang, H, Madabhushi, A, Cruz-Roa, A, Gonzalez, F, Larsen, ABL, Vestergaard, JS, Dahl, AB, Ciresan, DC, Schmidhuber, J, Giusti, A, Gambardella, LM, Tek, FB, Walter, T, Wang, C-W, Kondo, S, Matuszewski, BJ, Precioso, F, Snell, V, Kittler, J, de Campos, TE, Khan, AM, Rajpoot, NM, Arkoumani, E, Lacle, MM, Viergever, MA and Pluim, JPW (2015) Assessment of algorithms for mitosis detection in breast cancer histopathology images MEDICAL IMAGE ANALYSIS, 20 (1). pp. 237-248.

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Veta, MUNSPECIFIEDUNSPECIFIED
van Diest, PJUNSPECIFIEDUNSPECIFIED
Willems, SMUNSPECIFIEDUNSPECIFIED
Wang, HUNSPECIFIEDUNSPECIFIED
Madabhushi, AUNSPECIFIEDUNSPECIFIED
Cruz-Roa, AUNSPECIFIEDUNSPECIFIED
Gonzalez, FUNSPECIFIEDUNSPECIFIED
Larsen, ABLUNSPECIFIEDUNSPECIFIED
Vestergaard, JSUNSPECIFIEDUNSPECIFIED
Dahl, ABUNSPECIFIEDUNSPECIFIED
Ciresan, DCUNSPECIFIEDUNSPECIFIED
Schmidhuber, JUNSPECIFIEDUNSPECIFIED
Giusti, AUNSPECIFIEDUNSPECIFIED
Gambardella, LMUNSPECIFIEDUNSPECIFIED
Tek, FBUNSPECIFIEDUNSPECIFIED
Walter, TUNSPECIFIEDUNSPECIFIED
Wang, C-WUNSPECIFIEDUNSPECIFIED
Kondo, SUNSPECIFIEDUNSPECIFIED
Matuszewski, BJUNSPECIFIEDUNSPECIFIED
Precioso, FUNSPECIFIEDUNSPECIFIED
Snell, VUNSPECIFIEDUNSPECIFIED
Kittler, JUNSPECIFIEDUNSPECIFIED
de Campos, TEUNSPECIFIEDUNSPECIFIED
Khan, AMUNSPECIFIEDUNSPECIFIED
Rajpoot, NMUNSPECIFIEDUNSPECIFIED
Arkoumani, EUNSPECIFIEDUNSPECIFIED
Lacle, MMUNSPECIFIEDUNSPECIFIED
Viergever, MAUNSPECIFIEDUNSPECIFIED
Pluim, JPWUNSPECIFIEDUNSPECIFIED
Date : 1 February 2015
Identification Number : 10.1016/j.media.2014.11.010
Uncontrolled Keywords : Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Artificial Intelligence, Computer Science, Interdisciplinary Applications, Engineering, Biomedical, Radiology, Nuclear Medicine & Medical Imaging, Computer Science, Engineering, Breast cancer, Whole slide imaging, Digital pathology, Mitosis detection, Cancer grading, COUNTING MITOSES, SECTIONS, FEASIBILITY
Related URLs :
Additional Information : Copyright 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/
Depositing User : Symplectic Elements
Date Deposited : 18 Aug 2015 13:54
Last Modified : 01 Feb 2016 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/808087

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