Stacked Pointnets For Alignment Of Particles With Cylindrical Symmetry In Single Molecule Localization Microscopy

Single molecule localization microscopy is an essential observation tool in biology that yields data in the form of point clouds. It is still limited by an anisotropic resolution and inhomogeneous labeling density. This issue can be addressed by reconstructing a single model from multiple aligned copies of the same particle. However, generic registration methods fail to align point clouds in the presence of anisotropic noise and outliers. Therefore, we propose an alignment method dedicated to a common type of particle geometry, namely cylindrical symmetry. We focus on the centriole, a fundamental macromolecular assembly with ninefold cylindrical symmetry. We design a neural network based on stacked PointNet architectures that estimates the center and axis of symmetry of individual particles in SMLM, in order to align them in the same canonical space. We demonstrate the robustness of our approach on simulated and real dSTORM data.