Clinton S. Potter

The paper describes a Kaggle challenge for particle picking in cryo-electron tomography, resulting in improved algorithms and highlighting the need for better annotation quality measures.

This paper introduces a crossed laser phase plate (xLPP) for cryoEM, demonstrating improved contrast and resolution in imaging biological samples.

AreTomo3 and DenoisET form an automated GPU pipeline that reconstructs cryo-electron tomograms with tile-based contrast transfer function correction, screens their quality, and denoises them during acquisition.

The paper introduces open-source tools for a machine learning competition focused on particle picking in cryo-electron tomography data.

The paper introduces a phantom sample and annotated 3D volumes for a machine learning challenge to improve cryoET volume annotation.