CryoSIP: unleashing protein high-resolution Cryo-EM via semantic-instance collaborative picking.

Abstract

Precise particle picking in cryo-electron microscopy (cryo-EM) single-particle analysis constitutes a fundamental challenge in achieving high-resolution 3D reconstruction. To address low detection rates and elevated false positives caused by low signal-to-noise ratios (SNRs) and weak image contrast, we developed a novel semantic-instance collaborative picking framework. Key innovations include: (i) A multi-frequency adaptive U-Net framework that precisely localizes particles via global-context semantic modeling and multi-scale feature fusion; (ii) CryoSIP further enhances the collaborative optimization between SAM and U-Net by refining the interaction between SAM and U-Net-derived semantic priors, thereby improving instance mask generation. Experiments demonstrate that our framework reduces false positives significantly compared to state-of-the-art tools while achieving high recall in particle picking. 3D reconstructions using this approach exhibit improved density map resolution, demonstrating its potential for advancing atomic-resolution cryo-EM structural analysis.