SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals

TL;DR

SINETRA is a versatile simulation framework that generates synthetic, annotated videos to evaluate and improve neuron tracking algorithms in complex, dynamic animal behaviors.

Contribution

We introduce SINETRA, a novel simulator that creates realistic synthetic data for evaluating neuron tracking methods in challenging biological scenarios.

Findings

Current algorithms struggle with complex animal movements.

SINETRA effectively mimics real animal tracking conditions.

Evaluation highlights limitations of existing tracking methods.

Abstract

Accurately tracking neuronal activity in behaving animals presents significant challenges due to complex motions and background noise. The lack of annotated datasets limits the evaluation and improvement of such tracking algorithms. To address this, we developed SINETRA, a versatile simulator that generates synthetic tracking data for particles on a deformable background, closely mimicking live animal recordings. This simulator produces annotated 2D and 3D videos that reflect the intricate movements seen in behaving animals like Hydra Vulgaris. We evaluated four state-of-the-art tracking algorithms highlighting the current limitations of these methods in challenging scenarios and paving the way for improved cell tracking techniques in dynamic biological systems.