2025Ni GTPick

Citation

Ni, S., Yang, C., Liu, Y., Zhang, Y., Shi, Y., Qian, A., Kong, R. and Chang, S. 2025. GTpick: A Deep Neural Network for Cryo-EM Particle Detection. Computational and Structural Biotechnology Journal. (2025).

Abstract

Accurate identification of protein particles in cryo-electron microscopy (Cryo-EM) images is crucial for achieving high-resolution three-dimensional (3D) structural reconstruction. However, this task faces multiple challenges, including low signal-to-noise ratios, densely distributed particles, and class imbalance. To address these issues, this study proposes a target detection algorithm named GTpick, built upon the DETR framework. GTpick introduces a cross-attention mechanism to enhance the interaction between target queries and specific image features. In addition, a grouped one-to-many label assignment strategy is employed to improve recall in densely populated regions, and a Focal Loss function is incorporated to mitigate the adverse effects of background noise and class imbalance on detection accuracy. Experiments on large-scale Cryo-EM datasets demonstrate that GTpick outperforms existing machine learning-based particle-picking methods in terms of the resolution of 3D density maps reconstructed from detected particles and achieves superior Recall and F1 scores, particularly excelling in the Recall metric.

Links

https://www.sciencedirect.com/science/article/pii/S2001037025004337