A neural network model enables worm tracking in challenging conditions and increases signal-to-noise ratio in phenotypic screens.

Abstract excerpt

High-resolution posture tracking of C. elegans has applications in genetics, neuroscience, and drug screening. While classic methods can reliably track isolated worms on uniform backgrounds, they fail when worms overlap, coil, or move in complex environments. Model-based tracking and deep learning approaches have addressed these issues to an extent, but there is still significant room for improvement in tracking...

Topics

• Caenorhabditis elegans
• Animals
• Neural Networks, Computer
• Signal-To-Noise Ratio
• Algorithms
• Phenotype