Efficient double helix detection with steerable filters

We present an efficient detection scheme for localization of double helix point-spread functions for 3D single-molecule localization microscopy or tracking. Using steerable filters, we extract both 2D position and lobe orientation (axial position) estimates using just 7 convolutions, orders of magnitude less than used in deep-learning-based approaches. We pair this detection with a fitter and implement both as a plug-in for the open source Python Microscopy Environment (PYME), which features percentile-based background subtraction, signal-to-noise-based detection thresholding, and performant parallel analysis. Our complete localization analysis pipeline achieves state-of-the-art performance with minimal user input.