CryoQSeg : robust particle segmentation for cryo-EM with Qwen-Image-Edit
Abstract
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.
Related
- Particle picking and centering in cryo-electron micrographs via a vanishing radial kernel
- Reconstruction-Aware Cryo-EM Particle Picking
- Recovery of a minor cryo-EM particle population reveals conformational equilibria linking cofactor loading, turnover, and reactivation in methionine synthase
- Quantifying RNA Folding Equilibria from Unclassified Cryo-EM Particle Images
- Deep Learning for Protein Secondary-Structure Segmentation in Intermediate-Resolution Cryo-EM Maps: A Resolution-Stratified Benchmark Dataset and Controlled Evaluation
BibTeX
@article{sun2026cryoqseg,
title = {{CryoQSeg : robust particle segmentation for cryo-EM with Qwen-Image-Edit}},
author = {Yunhai Sun and Jiahao Zhao and Nan Xu and Lei Wang and Wei Ding and Ming Li},
journal = {IUCrJ},
year = {2026},
month = oct,
doi = {10.1107/s2052252526009887},
url = {https://journals.iucr.org/paper?S2052252526009887},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - CryoQSeg : robust particle segmentation for cryo-EM with Qwen-Image-Edit
AU - Yunhai Sun
AU - Jiahao Zhao
AU - Nan Xu
AU - Lei Wang
AU - Wei Ding
AU - Ming Li
PY - 2026
DA - 2026/10/15
JO - IUCrJ
DO - 10.1107/s2052252526009887
UR - https://journals.iucr.org/paper?S2052252526009887
AB - 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.
SN - 2052-2525
ER -