An Active Learning Framework for ML-Assisted Labeling of Cryo-EM Micrographs

Single-particle cryo-electron microscopy (cryo-EM) has grown significantly as a tool for discerning biological macromolecule structures. A fundamental step in this technique is the accurate identification of individual protein particles from micrographs laden with noise. Machine learning models, specifically convolutional neural networks like ResNet, have shown promise by reducing dependence on manual methods and adapting to the intricate features within the micrographs. However, challenges persist due to low signal-to-noise ratios, resulting in false positives or missed detections. Analogous challenges in computer vision have found respite in active learning, a method that combines automated systems with human intervention for refined outcomes. This paper presents a novel approach for cryo-EM particle picking based on active learning and logistic regression. Our method employs the pre-trained convolutional-based model from the Topaz particle picking software. This model is used for the initial feature extraction and subsequently refines particle predictions through a logistic regression with a human feedback loop. Complementing this, we introduce the Napari plugin, enhancing user interaction with the micrograph and facilitating intuitive model training. This approach allowed us to achieve $\sim$ 10\% average precision improvement over the Topaz pre-trained model with only 100 labeled particles.