From Unlabeled Micrographs to 3D Structure: Self-Supervised Reconstruction with Score Models

Single-particle cryo-electron microscopy (cryo-EM) aims to recover the 3D structure of biological macromolecules from large, noisy 2D micrographs. Each micrograph contains multiple particle projections at unknown positions and orientations, corrupted by the contrast transfer function and high noise levels. Standard cryo-EM pipelines rely on particle picking to extract candidate particles from micrographs, followed by a sequence of processing stages for alignment, classification, and 3D reconstruction. We introduce a self-supervised framework that removes the need for explicit particle picking by learning the distribution of random micrograph crops via denoising score matching. The learned score function is first used for fully self-supervised denoising through a Noisier2Noise estimator, producing noise-reduced micrographs without clean supervision. The score model is then fine-tuned on crops from these denoised micrographs to better capture underlying structural variability. At inference time, a user specifies a region of interest in a denoised micrograph, and a 3D volume is reconstructed using a score-distillation-based procedure. This approach replaces manual or heuristic particle picking in noisy or contrast-enhanced micrographs with a learned generative representation. We validate the method on two challenging simulated datasets and show that it can handle both conformational and compositional heterogeneity.