SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals
This work addresses the lack of annotated datasets for tracking neuronal activity in behaving animals, which is a problem for researchers in neuroscience and bioimaging, though it is incremental as it focuses on simulation and evaluation rather than a new tracking method.
The authors tackled the problem of evaluating single neuron tracking in behaving animals by developing SINETRA, a versatile simulator that generates synthetic annotated 2D and 3D videos mimicking live recordings, and used it to assess four state-of-the-art tracking algorithms, revealing their limitations in challenging scenarios.
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.