# Cell tracking in low-frame-rate video based on displacement prediction

> **NIH NIH R21** · CLEMSON UNIVERSITY · 2024 · $177,197

## Abstract

Project Summary
Tracking living cells in video sequences is a fundamental task in many fields of science,
including biochemistry, bioinformatics, cell biology, and genetics. Manually linking cells is
extremely time-consuming and not feasible in large-scale analysis. Automatic approaches can
compute cell links by measuring how close two instances of a cell are, or how similar they look.
These techniques work well with video acquired at a relatively high frame rate, but,
unfortunately, acquiring images at high frame rates affects cells negatively. Too frequent
imaging not only causes phototoxicity, leading to experimental artifacts, but also
photobleaching, leading to the inability to measure quantities of interest over time. In addition,
during image acquisition, the environment temperature and air quality are typically less
controlled, which could also contribute to cytotoxicity. Moreover, when performing high-
throughput live-cell imaging, the lower the acquisition rate, the more cells/plates can be imaged,
and, consequently, the more experimental treatments can be applied and studied.
If reducing the acquisition rate is beneficial for all these reasons, it severely affects the accuracy
of cell tracking algorithms. To this end, we propose a new class of cell tracking approaches based
on cell movement predictions. Instead of comparing cells based on their similarity, we propose
to predict where every cell will move in the next frame. This will allow for searching the
occurrence of such cells, even if the next frame was acquired after an extended period. The
new approach will be investigated using a newly generated dataset for low frame rate cell
tracking (Aim 1). Cell displacement will be predicted by using a new Recurrent Neural Network
designed for the task (Aim 2). Cell tracking algorithms will be defined re-evaluating existing
approaches under low-frame rate constraints when using cell displacement information (Aim
3).
While current approaches require image acquisition to occur at least every 5-15 minutes, we
will investigate the feasibility of cell tracking on images acquired at intervals of up to 2 hours. If
successful, our research will allow to accurately track cells in low frame rate video sequences
without the need for specialized tools or equipment.

## Key facts

- **NIH application ID:** 10915409
- **Project number:** 5R21GM150066-02
- **Recipient organization:** CLEMSON UNIVERSITY
- **Principal Investigator:** Federico Iuricich
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $177,197
- **Award type:** 5
- **Project period:** 2023-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10915409

## Citation

> US National Institutes of Health, RePORTER application 10915409, Cell tracking in low-frame-rate video based on displacement prediction (5R21GM150066-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10915409. Licensed CC0.

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