Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through
  Learning Offsets and Clustering Bandwidths   A systematic analysis of the cell behavior requires automated approaches for
cell segmentation and tracking. While deep learning has been successfully
applied for the task of cell segmentation, 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 easy to interpret embeddings. As
embeddings, offsets of cell pixels to their cell center and bandwidths are
learned. 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.

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The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ