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

Raphael Reme, Alasdair Newson, Elsa Angelini, Jean-Christophe Olivo-Marin, Thibault Lagache
11/14/2024
cs.CV

No official code found. Best implementation: raphaelreme/SINETRA (8 stars, MIT, last commit 2025-10-08).

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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Code Implementations(1)

raphaelreme/SINETRACommunity100%
MIT

Code of the SINETRA synthetic dataset

80Shell, PythonOct 1, 20241 years agoMIT

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Cite this paper

@article{reme2024sinetra,
  title  = {SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals},
  author = {Raphael Reme and Alasdair Newson and Elsa Angelini and Jean-Christophe Olivo-Marin and Thibault Lagache},
  year   = {2024},
  eprint = {2411.09462},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.09462v2}
}

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