| Literature DB >> 25547073 |
Mitko Veta1, Paul J van Diest2, Stefan M Willems2, Haibo Wang3, Anant Madabhushi3, Angel Cruz-Roa4, Fabio Gonzalez4, Anders B L Larsen5, Jacob S Vestergaard5, Anders B Dahl5, Dan C Cireşan6, Jürgen Schmidhuber6, Alessandro Giusti6, Luca M Gambardella6, F Boray Tek7, Thomas Walter8, Ching-Wei Wang9, Satoshi Kondo10, Bogdan J Matuszewski11, Frederic Precioso12, Violet Snell13, Josef Kittler13, Teofilo E de Campos14, Adnan M Khan15, Nasir M Rajpoot16, Evdokia Arkoumani17, Miangela M Lacle2, Max A Viergever18, Josien P W Pluim18.
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.Entities:
Keywords: Breast cancer; Cancer grading; Digital pathology; Mitosis detection; Whole slide imaging
Mesh：
Year: 2014 PMID： 25547073 DOI： 10.1016/j.media.2014.11.010
Source DB: PubMed Journal: Med Image Anal ISSN： 1361-8415 Impact factor: 8.545