Cell Tracking according to Biological Needs -- Strong Mitosis-aware Random-finite Sets Tracker with Aleatoric Uncertainty

• URL: http://arxiv.org/abs/2403.15011v2
• Date: Mon, 25 Mar 2024 14:50:47 GMT
• Title: Cell Tracking according to Biological Needs -- Strong Mitosis-aware Random-finite Sets Tracker with Aleatoric Uncertainty
• Authors: Timo Kaiser, Maximilian Schier, Bodo Rosenhahn,
• Abstract summary: We introduce an uncertainty estimation technique for neural tracking-by-regression frameworks. Our uncertainty estimation identifies uncertain associations within high-performing tracking-by-regression methods. Our tracker resolves false associations and mitosis detections stemming from long-term conflicts.
• Score: 20.015078699404143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cell tracking and segmentation assist biologists in extracting insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches often suffer from a lack of long-term consistency. To address this issue, we introduce an uncertainty estimation technique for neural tracking-by-regression frameworks and incorporate it into our novel extended Poisson multi-Bernoulli mixture tracker. Our uncertainty estimation identifies uncertain associations within high-performing tracking-by-regression methods using problem-specific test-time augmentations. Leveraging this uncertainty, along with a novel mitosis-aware assignment problem formulation, our tracker resolves false associations and mitosis detections stemming from long-term conflicts. We evaluate our approach on nine competitive datasets and demonstrate that it outperforms the current state-of-the-art on biologically relevant metrics substantially, achieving improvements by a factor of approximately $5.75$. Furthermore, we uncover new insights into the behavior of tracking-by-regression uncertainty.

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