Counting or Localizing? Evaluating Cell Counting and Detection in Microscopy Images

Image-based automatic cell counting is an essential yet challenging task, crucial for the diagnosing of many diseases. Current solutions rely on Convolutional Neural Networks and provide astonishing results. However, their performance is often measured only considering counting errors, which can lead to masked mistaken estimations; a low counting error can be obtained with a high but equal number of false positives and false negatives. Consequently, it is hard to determine which solution truly performs best. In this work, we investigate three general counting approaches that have been successfully adopted in the literature for counting several different categories of objects. Through an experimental evaluation over three public collections of microscopy images containing marked cells, we assess not only their counting performance compared to several state-of-the-art methods but also their ability to correctly localize the counted cells. We show that commonly adopted counting metrics do not always agree with the localization performance of the tested models, and thus we suggest integrating the proposed evaluation protocol when developing novel cell counting solutions.