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

• URL: http://arxiv.org/abs/2204.10713v2
• Date: Mon, 25 Apr 2022 06:11:34 GMT
• Title: EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
• Authors: Katharina L\"offler and Ralf Mikut
• Abstract summary: We present EmbedTrack, a single convolutional neural network for simultaneous cell segmentation and tracking. 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.
• Score: 0.30458514384586405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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