Abstract
The tracking-by-detection strategy is the backbone of many methods for tracking living cells in time-lapse microscopy. An object detector is first applied to the input images, and the resulting detection candidates are then linked by a data association module. The performance of such methods strongly depends on the quality of the detector because detection errors propagate to the linking step. To tackle this issue, we propose a joint model for segmentation, detection and tracking. The model is defined implicitly as limiting distribution of a Markov chain Monte Carlo algorithm and contains a temporal feedback, which allows to dynamically alter detector parameters using hints given by neighboring frames and, in this way, correct detection errors. The proposed method can integrate any detector and is therefore not restricted to a specific domain. The parameters of the model are learned using an objective based on empirical risk minimization. We use our method to conduct large-scale experiments for confluent cultures of endothelial cells and evaluate its performance in the ISBI Cell Tracking Challenge, where it consistently scored among the best three methods.








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This work was supported by the Czech Science Foundation project 16-05872S and by the Graduate School of the Cells-in-Motion Cluster of Excellence (EXC 1003 - CiM), WWU Münster and International Max Planck Research School – Molecular Biomedicine, Münster. HS acknowledges grants SCHN 43076-2 and DFG INST 2105/24-1 of the German Research Council and grants 03ZZ0902D and 03ZZ0906E from the BMBF. The supports by the Excellence Cluster Cells In Motion (CIM) flexible fund to J.S (FF-2016-15) and to HS (FF-2014-15) are also greatly acknowledged. BF gratefully acknowledges support by the Czech OP VVV project “Research Center for Informatics” (CZ.02.1.01/0.0/0.0/16 019/0000765).
A Experiments with endothelial cells
A Experiments with endothelial cells
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E1:
Cells treated with 50 ng/ml vascular endothelial growth factor (VEGF) versus cells treated with phosphate-buffered saline (control)
Group 1: 4 sequences, 1646 cells
Group 2: 4 sequences, 1701 cells
Duration: 6 h (73 frames)
Objective: \(10\times \) (\(1.02~\upmu \)m per pixel)
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E2:
Cells transfected with adenoN17Rac virus (adenoN17Rac) \(+\) VEGF versus cells transfected with adeno-empty virus \(+\) VEGF (control)
Group 1: 3 sequences, on average 364 cells in each sequence
Group 2: 3 sequences, 377 cells
Duration: 26 h (79 frames)
Objective: \(20\times \) (\(0.215~\upmu \)m per pixel)
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E3:
Cells treated with the EHT1864 inhibitor of Rac activity (EHT1864) \(+\) VEGF versus cells treated with dimethyl sulfoxide \(+\) VEGF (control)
Group 1: 2 sequences, 1700 cells
Group 2: 3 sequences, 1520 cells
Duration: 19 h (77 frames)
Objective: \(10\times \) (\(1.02~\upmu \)m per pixel)
-
E4:
Cells transfected with Nrp siRNA (siNrp) \(+\) VEGF versus cells transfected with non-targeting siRNA \(+\) VEGF (control)
Group 1: 3 sequences, 1221 cells
Group 2: 3 sequences, 1272 cells
Duration: 24.5 h (50 frames)
Objective: \(10\times \) (\(1.02~\upmu \)m per pixel)
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E5:
Cells transfected with vascular endothelial growth factor receptor 2 siRNA (siVEGFR2) \(+\) VEGF versus cells transfected with non targeting siRNA \(+\) VEGF (control)
Group 1: 3 sequences, 1132 cells
Group 2: 3 sequences, 1390 cells
Duration: 22.5 h (46 frames)
Objective: \(10\times \) (\(1.02~\upmu \)m per pixel)
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E6:
Cells transfected with VE-cadherin-GFP adenovirus
(VEcadGFP) \(+\) VEGF versus cells transfected with GFP adenovirus \(+\) VEGF (control)
Group 1: 3 sequences, 387 cells
Group 2: 3 sequences, 397 cells
Duration: 26 h (157 frames)
Objective: \(20\times \) (\(0.215~\upmu \)m per pixel)
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Sixta, T., Cao, J., Seebach, J. et al. Coupling cell detection and tracking by temporal feedback. Machine Vision and Applications 31, 24 (2020). https://doi.org/10.1007/s00138-020-01072-7
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DOI: https://doi.org/10.1007/s00138-020-01072-7