Cryo ET

Cryo-electron tomography (cryo-ET) is a 3D imaging technique used to visualize biological macromolecules at near-atomic resolution, aiming to understand their structure and function within their cellular context. Current research focuses on improving automated image analysis, particularly segmentation and classification of macromolecules within cryo-ET datasets, often employing deep learning architectures like U-Nets and novel algorithms to address challenges such as low signal-to-noise ratios and limited labeled data. These advancements are crucial for accelerating biological discovery by enabling high-throughput analysis of complex cellular structures and improving the accuracy of structural determination. Unsupervised domain adaptation techniques are being actively developed to overcome the limitations of relying solely on manually labeled data.