Assessment of algorithms for mitosis detection in breast cancer histopathology images · SciRah

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Assessment of algorithms for mitosis detection in breast cancer histopathology images

Mitko Veta^{a b}✉, P. J. van Diest^c, Stefan M. Willems^c, Haibo Wang^d, Anant Madabhushi^d, Ángel Cruz-Roa^e, Fabio Augusto Gonzalez^e, Anders Larsen^f, Jacob S. Vestergaard^f, Anders Bjorholm Dahl^f, Dan Claudiu Ciresan^{g h},

Medical Image Analysis Elsevier BV2014-11-29 480 citations38 references

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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.

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^aImage Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands. Electronic address: mitko@isi.uu.nl
^bImage Sciences Institute University Medical Center Utrecht Utrecht the Netherlands
^cPathology Department, University Medical Center Utrecht, Utrecht, The Netherlands
^dBME Department, Case Western Reserve University, Cleveland, USA
^eMindLab, National University of Colombia, Bogota, Colombia
^fDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
^gIDSIA, USI-SUPSI, Lugano, Switzerland
^hIDSIA - USI - SUPSI, Lugano, Switzerland
ⁱDepartment of Computer Engineering, Işık University, İstanbul, Turkey
^jDepartment of Computer Engineering, Isik University, Istanbul, Turkey
^kMINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, France
^lInstitut Curie, Paris, France
^mINSERM U900, Paris, France
ⁿGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
^o[Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan]
^pCorporate R&D Center, Panasonic Healthcare Co., Ltd., Osaka, Japan
^qCurrent address: Konica Minolta, Inc., Japan
^rSchool of Computing, Engineering and Physical Sciences, University of Central Lancashire, Preston, UK
^sI3S, UMR 7271 UNS-CNRS, Nice Sophia-Antipolis University, Sophia Antipolis, France
^tCVSSP, University of Surrey, Guildford, UK
^uCVSSP, University of Surrey, Guildford, UK#TAB#
^vDepartment of Computer Science and Sheffield Institute for Translational Medicine, University of Sheffield, UK
^wDepartment of Computer Science, University of Warwick, UK
^x[Dept of Computer Science, University of Warwick, UK]
^yDepartment of Computer Science & Engineering, College of Engineering Qatar University, Doha, Qatar
^zThe Michael Letcher Department of Cellular Pathology, Princess Alexandra Hospital NHS Trust, Harlow, Essex, UK
^aaImage Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
✉Corresponding author