Cell as Point: One-Stage Framework for Efficient Cell Tracking

2024-11-22

arXiv.org

AI Summary

Traditional multi-stage cell tracking relies on frame-by-frame detection or segmentation, incurring high computational overhead, expensive manual annotation, and poor robustness to class imbalance and long-sequence tracking. To address these limitations, we propose CAP, an end-to-end single-stage framework that models cells as spatiotemporal points and jointly learns trajectory prediction and visibility state estimation—eliminating detection or segmentation modules entirely. We introduce Adaptive Event-Guided (AEG) sampling to mitigate class imbalance caused by cell division events, and design a Rolling-Window-based (RAW) inference mechanism to ensure continuous tracking of newly emerging cells over extended sequences. CAP operates directly on raw image sequences without requiring segmentation masks or bounding boxes. Evaluated on multiple benchmarks, CAP achieves state-of-the-art accuracy while accelerating inference by 10–55× over existing methods, significantly improving efficiency, robustness, and scalability.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Cellular activities are dynamic and intricate, playing a crucial role in advancing diagnostic and therapeutic techniques, yet they often require substantial resources for accurate tracking. Despite recent progress, the conventional multi-stage cell tracking approaches not only heavily rely on detection or segmentation results as a prerequisite for the tracking stage, demanding plenty of refined segmentation masks, but are also deteriorated by imbalanced and long sequence data, leading to under-learning in training and missing cells in inference procedures. To alleviate the above issues, this paper proposes the novel end-to-end CAP framework, which leverages the idea of regarding Cell as Point to achieve efficient and stable cell tracking in one stage. CAP abandons detection or segmentation stages and simplifies the process by exploiting the correlation among the trajectories of cell points to track cells jointly, thus reducing the label demand and complexity of the pipeline. With cell point trajectory and visibility to represent cell locations and lineage relationships, CAP leverages the key innovations of adaptive event-guided (AEG) sampling for addressing data imbalance in cell division events and the rolling-as-window (RAW) inference method to ensure continuous tracking of new cells in the long term. Eliminating the need for a prerequisite detection or segmentation stage, CAP demonstrates strong cell tracking performance while also being 10 to 55 times more efficient than existing methods. The code and models will be released.

Problem

• Eliminates need for explicit cell detection or segmentation
• Addresses data imbalance in capturing cell division events
• Improves efficiency in long-sequence cell tracking

Innovation

• CAP treats cells as points, eliminating segmentation needs.
• AEG sampling prioritizes cell division events effectively.
• RAW strategy ensures stable tracking in long sequences.