BeadNet: Deep learning-based bead detection and counting in low-resolution microscopy images

Abstract

Motivation: An automated counting of beads is required for many high-throughput experiments such as studying mimicked bacterial invasion processes. However, state-of-the-art algorithms under- or overestimate the number of beads in low-resolution images. In addition, expert knowledge is needed to adjust parameters. Results: In combination with our image labeling tool, BeadNet enables biologists to easily annotate and process their data reducing the expertise required in many existing image analysis pipelines. BeadNet outperforms state-of-the-art-algorithms in terms of missing, added and total amount of beads. Availability and implementation: BeadNet (software, code and dataset) is available at https://bitbucket.org/t_scherr/ beadnet. The image labeling tool is available at https://bitbucket.org/abartschat/imagelabelingtool.