Efficient Double Helix Detection with Steerable Filters
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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. For a complete SMLM analysis pipeline, we pair this detection with a fitter using an optimally parameterized double Gaussian model, and implement both as a plugin for the open source PYthon Microscopy Environment (PYME).
