Augmented Mitotic Cell Count using Field Of Interest Proposal

TL;DR

This paper introduces an algorithm that automatically proposes the most mitotically active region in histopathology images to improve tumor grading consistency, using deep learning to estimate mitotic activity.

Contribution

The novel approach combines deep learning with region proposal to enhance the accuracy and reproducibility of mitotic cell counting in histopathology.

Findings

High correlation (r=0.936) between estimated and actual mitotic counts.

Effective identification of regions with highest mitotic activity.

Potential to reduce variability in tumor grading.

Abstract

Histopathological prognostication of neoplasia including most tumor grading systems are based upon a number of criteria. Probably the most important is the number of mitotic figures which are most commonly determined as the mitotic count (MC), i.e. number of mitotic figures within 10 consecutive high power fields. Often the area with the highest mitotic activity is to be selected for the MC. However, since mitotic activity is not known in advance, an arbitrary choice of this region is considered one important cause for high variability in the prognostication and grading. In this work, we present an algorithmic approach that first calculates a mitotic cell map based upon a deep convolutional network. This map is in a second step used to construct a mitotic activity estimate. Lastly, we select the image segment representing the size of ten high power fields with the overall highest mitotic activity as a region proposal for an expert MC determination. We evaluate the approach using a dataset of 32 completely annotated whole slide images, where 22 were used for training of the network and 10 for test. We find a correlation of r=0.936 in mitotic count estimate.