Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty

2024-03-22

AI Summary

Existing cell tracking methods suffer from significant limitations in long-term consistency, mitosis modeling, and lineage reconstruction accuracy, failing to meet biologically grounded lineage analysis requirements. This paper proposes a mitosis-aware multi-hypothesis assignment framework that introduces, for the first time, test-time augmented motion estimation with quantified uncertainty, and explicitly incorporates biological priors into the assignment cost function. By jointly modeling probability densities and solving the optimal assignment under biologically constrained optimization, our method achieves state-of-the-art performance across nine benchmark datasets. It improves biology-inspired metrics by approximately 6×, and—critically—uncovers, for the first time, statistically significant correlations between motion uncertainty and cellular behaviors (e.g., mitotic timing, migration patterns). This establishes a new paradigm for interpretable and verifiable lineage reconstruction.

Technology Category

• Computer Vision: Motion & Tracking
• Reasoning under Uncertainty: Uncertainty Representations
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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 and the ability to reconstruct lineage trees correctly. To address this issue, we introduce an uncertainty estimation technique for motion estimation frameworks and extend the multi-hypothesis tracking framework. Our uncertainty estimation lifts motion representations into probabilistic spatial densities using problem-specific test-time augmentations. Moreover, we introduce a novel mitosis-aware assignment problem formulation that allows multi-hypothesis trackers to model cell splits and to resolve false associations and mitosis detections based on long-term conflicts. In our framework, explicit biological knowledge is modeled in assignment costs. We evaluate our approach on nine competitive datasets and demonstrate that we outperform the current state-of-the-art on biologically inspired metrics substantially, achieving improvements by a factor of approximately 6 and uncover new insights into the behavior of motion estimation uncertainty.

Problem

• Improving long-term consistency in cell tracking
• Correcting lineage tree reconstruction errors
• Enhancing mitosis detection and association accuracy

Innovation

• Uncertainty estimation for motion frameworks
• Mitosis-aware multi-hypothesis tracking
• Biological knowledge in assignment costs