Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training
This provides an incremental improvement for neuroscientists studying C. elegans by enabling faster and more accurate neuron correspondence without requiring preprocessing steps like straightening.
The authors tackled the problem of automated neuron tracking and identification in C. elegans by developing fDLC, a transformer-based method trained on synthetic data, which achieved 80.0% accuracy for tracking within individuals and 65.8% accuracy for identification across individuals.
We present an automated method to track and identify neurons in C. elegans, called "fast Deep Learning Correspondence" or fDLC, based on the transformer network architecture. The model is trained once on empirically derived synthetic data and then predicts neural correspondence across held-out real animals via transfer learning. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL [1]. Using only position information, the method achieves 80.0% accuracy at tracking neurons within an individual and 65.8% accuracy at identifying neurons across individuals. Accuracy is even higher on a published dataset [2]. Accuracy reaches 76.5% when using color information from NeuroPAL. Unlike previous methods, fDLC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10 ms making it suitable for future real-time applications.