Assessment of algorithms for mitosis detection in breast cancer histopathology images

TL;DR

This paper evaluates various algorithms for detecting mitotic figures in breast cancer histopathology images, highlighting the challenges and comparing the performance of different methods in a standardized challenge setting.

Contribution

It provides a comprehensive assessment of eleven mitosis detection algorithms using a large annotated dataset from the AMIDA13 challenge, establishing performance benchmarks.

Findings

Top method has error rate comparable to inter-observer variability.

Automatic detection approaches can match human performance in mitosis counting.

The challenge dataset enables standardized comparison of algorithms.

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.