Cell Segmentation and Tracking using Distance Transform Predictions and Movement Estimation with Graph-Based Matching
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In this paper, we present the approach used for our IEEE ISBI 2020 Cell Tracking Challenge contribution (team KIT-Sch-GE). Our method consists of a segmentation and a tracking step that includes the correction of segmentation errors (tracking by detection method). For the segmentation, deep learning-based predictions of cell distance maps and novel neighbor distance maps are used as input for a watershed post-processing. Since most of the provided Cell Tracking Challenge ground truth data are 2D, a 2D convolutional neural network is trained to predict the distance maps. The tracking is based on a movement estimation in combination with a matching formulated as a maximum flow minimum cost problem.
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