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A systematic analysis of the cell behavior requires automated approaches forcell segmentation and tracking. While deep learning has been successfullyapplied for the task of cell segmentation, there are few approaches forsimultaneous cell segmentation and tracking using deep learning. Here, wepresent EmbedTrack, a single convolutional neural network for simultaneous cellsegmentation and tracking which predicts easy to interpret embeddings. Asembeddings, offsets of cell pixels to their cell center and bandwidths arelearned. We benchmark our approach on nine 2D data sets from the Cell TrackingChallenge, where our approach performs on seven out of nine data sets withinthe top 3 contestants including three top 1 performances. The source code ispublicly available at https://git.scc.kit.edu/kit-loe-ge/embedtrack.

EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths