Title: Unsupervised SE(3) Disentanglement for in situ Macromolecular Morphology Identification from Cryo-Electron Tomography
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Disentangled Representation Learning, SE(3) Estimation, Inverse Problems, Cryo-ET

Abstract: Cryo-electron tomography (cryo-ET) provides direct 3D visualization of macromolecules inside the cell, enabling analysis of their \textit{in situ} morphology. This morphology can be regarded as an $SE(3)$-invariant, denoised volumetric representation of subvolumes extracted from tomograms. Inferring morphology is therefore an inverse problem of estimating both a template morphology and its SE(3) transformation. Existing maximum likelihood–based solution to this problem often miss rare but important morphologies and require extensive manual hyperparameter tuning. Addressing this issue, we present a disentangled deep representation learning framework that separates SE(3) transformations from morphological content in the representation space. The framework includes a novel multi-choice learning module that enables this disentanglement for highly noisy cryo-ET data, and the learned morphological content is used to generate template morphologies. Experiments on simulated and real cryo-ET datasets demonstrate clear improvements over prior methods, including the discovery of previously unidentified macromolecular morphologies.

TL;DR: A novel SE(3) disentanglement method enabling macromolecular morphology identification