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

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

URL: http://arxiv.org/abs/2411.09462v2
Date: Fri, 15 Nov 2024 12:41:48 GMT
Title: SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals
Authors: Raphael Reme, Alasdair Newson, Elsa Angelini, Jean-Christophe Olivo-Marin, Thibault Lagache,
Abstract summary: SINETRA is a versatile simulator that generates synthetic tracking data for particles on a deformable background. This simulator produces annotated 2D and 3D videos that reflect the intricate movements seen in behaving animals like Hydra Vulgaris.
Score: 7.039426581802364
License:
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.

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