No cell left behind: automated physics-based tracking of {\em every} cell in a dense and growing colony

Huy Pham; Emile Ramez Shehada; Shawna Stahlheber; Wayne B. Hayes

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arXiv · 1709.09155

No cell left behind: automated physics-based tracking of {\em every} cell in a dense and growing colony

Huy Pham·Emile Ramez Shehada·Shawna Stahlheber·Wayne B. Hayes

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Abstract

A human watching a video of closely-packed cells can generally identify every individual cell, regardless of density and noise, but most currently-available cell-tracking software cannot. This is because the human brain automatically builds a physical model of the scene as it progresses, allowing it to readily distinguish cells from noise and not be unduly confused by overlapping cells. Here we introduce software that uses physical rules to create a simulation of the activity in a cell video, synchronizing itself with the video as the activity progresses. Because our simulation includes every individual cell, we are trivially able to track all cell movement, growth, and divisions. Our method is also particularly robust to noise without requiring any substantial image processing. We demonstrate the effectiveness of this method by tracking the motion and lineage tree of a densely-packed colony of cells that grows from 4 to more than 200 individuals.

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Huy Pham, Emile Ramez Shehada, Shawna Stahlheber, Wayne B. Hayes. 2017-09-26. No cell left behind: automated physics-based tracking of {\em every} cell in a dense and growing colony. https://arxiv.org/abs/1709.09155

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