Cryo-electron tomography (cryo-ET) provides direct 3D visualization of macromolecules inside the cell, enabling analysis of their 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 expectation-maximization based solution to this problem often misses rare but important morphologies and requires 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.

翻译：冷冻电子断层扫描（cryo-ET）可直接三维可视化细胞内的大分子，从而支持对其原位形态的分析。该形态可视为从断层图中提取的子体积经SE(3)不变性处理后的去噪体积表征。因此，形态推断是一个同时估计模板形态及其SE(3)变换的逆问题。现有基于期望最大化的解决方案常遗漏罕见但重要的形态，且需大量人工超参数调优。针对此问题，我们提出一种解耦深度表征学习框架，在表征空间中将SE(3)变换与形态内容分离。该框架包含一个新颖的多选择学习模块，可在高噪声cryo-ET数据中实现解耦，所学习的形态内容用于生成模板形态。在模拟和真实cryo-ET数据集上的实验表明，本方法较现有技术有明显改进，包括发现了先前未识别的大分子形态。