Stamped counting for biomedical images

Abstract Counting objects is an important task in biomedical image analysis, for example to count cells in microscopy or microbial colonies in laboratory applications. Similar to most image analysis tasks, modern approaches to counting rely on deep learning, predominantly by solving an object detection or instance segmentation task and deriving the count from it. Here, we revisit regression-based counting, a simpler approach to counting, introducing a new method called STACC that makes use of the object size to derive the density map used as regression target. In our experiments, it consistently performs better than state-of-the-art methods automated for cell and microbial counting. We also provide a user-friendly tool to apply it to counting problems in practice.