A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

Abstract

Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation—including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation

“…22,23 The latter method (ii) has proven its applicability for phase contrast, bright field and fluorescence images for diverse cell morphologies in the Cell Tracking Challenge (http://celltrackingchallenge.net/). 22,24 The simple boundary method is easy to interpret and serves as a baseline.…”

Towards a comprehensive approach for characterizing cell activity in low contrast microscopic images

“…In our study, the time resolution is sufficient in comparison to the average cell's propagation velocity. Therefore, we are able to precisely track the individual cells by comparing overlapping labels in neighboring frames 23,44 . We compare each island in one frame to each island in the next frame through superposition and numerical label the individual cells by pixel area (from large to small area starting with zero for the background).…”

“…There have been numerous advancements in cell tracking e.g. graph-based methods 23 or recurrent neural networks (RNN) 24 .…”

Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion

“…Deep learning methods based on graphs are typically referred to as graph neural networks (GNNs) [16] and have been successfully applied, e.g., to molecular property prediction [17], drug discovery [18], and computerassisted retrosynthesis [19]. Besides being ubiquitously used in science to represent complex systems [20], graphs provide a natural and intuitive way to represent the information contained in tracking experiments [21,22].…”