CryoQSeg: robust particle segmentation for cryo-EM with Qwen-Image-Edit

Accurate particle picking remains a critical step in cryo-electron microscopy (cryo-EM) single-particle analysis, and the field has witnessed the development of a broad spectrum of approaches - from classical template-based methods to modern deep learning frameworks - to address this task. Here we present CryoQSeg, a particle segmentation and picking framework that represents a paradigm shift toward leveraging a state-of-the-art visual large model. Built upon Qwen-Image-Edit-2511 as its core backbone, CryoQSeg is fine-tuned through supervised fine-tuning and reinforcement learning on the public CryoPPP benchmark dataset, enabling the generation of high-fidelity, pixel-level particle masks directly from raw micrographs. To translate these masks into precise particle coordinates, we further integrate a hybrid post-processing pipeline that couples Hough circle detection with the segment anything model, forming a seamless end-to-end workflow from raw image input to accurate localization. We evaluate CryoQSeg on five representative benchmark datasets, demonstrating competitive performance across a range of protein types and imaging conditions in comparison to mainstream methods including Topaz and CryoSegNet. Furthermore, we validate the framework on the heterogeneous Crt-SPARTA-gRNA-tDNA complex, where it successfully resolves three distinct oligomeric states - monomer, dimer and tetramer - at high resolutions, despite using a fraction of the micrographs employed in the original study. By bridging modern visual large models with classical and foundation-model-based post-processing strategies, CryoQSeg demonstrates the potential of this emerging paradigm to deliver a robust and automated solution for cryo-EM particle picking, thereby contributing to a solid foundation for high-resolution three-dimensional structural determination of biological macromolecules.