EmbedTrack—Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
Katharina Löffler, Ralf Mikut
Abstract
To shed light on the processes driving cell migration, a systematic analysis of the cell behavior is required. Since the manual analysis of hundreds or even thousands of cells is infeasible, automated approaches for cell segmentation and tracking are needed. While for the task of cell segmentation deep learning has become the standard, there are few approaches for simultaneous cell segmentation and tracking using deep learning. Here, we present EmbedTrack, a single convolutional neural network for simultaneous cell segmentation and tracking which predicts human comprehensible embeddings. As embeddings, offsets of cell pixels to their cell center and bandwidths are learned which are processed in a subsequent clustering step to generate an instance segmentation and link the segmented instances over time. We benchmark our approach on nine 2D data sets from the Cell Tracking Challenge, where our approach performs on seven out of nine data sets within the top 3 contestants including three top 1 performances. The source code is publicly available at https://git.scc.kit.edu/kit-loe-ge/embedtrack.