RETRACTED ARTICLE: A hybrid YOLO-UNet3D framework for automated protein particle annotation in Cryo-ET images
Ziyang Liu, Chunhong Yuan, Zixin Zhang, Xiang Zhou, Xiangyu Li, Zhen Tian, Zhen Tian, Zuowen Jiang, Zhikui Tian, Zhikui Tian, Zhikui Tian
Abstract
Accurate localization and identification of protein complexes in cryo-electron tomography (cryo-ET) volumes are essential for understanding cellular functions and disease mechanisms. However, automated annotation of these macromolecular assemblies remains challenging due to low signal-to-noise ratios, missing wedge artifacts, heterogeneous backgrounds, and structural diversity. In this study, we present a hybrid framework integrating You Only Look Once (YOLO) object detection with UNet3D volumetric segmentation, enhanced by density-based spatial clustering of applications with noise (DBSCAN) post-processing for automated protein particle annotation in cryo-ET volumes. Our approach combines YOLO's efficient region proposal capabilities with UNet3D's powerful 3D feature extraction through a dual-branch architecture featuring optimized Spatial Pyramid Pooling-Fast (SPPF) modules and asymmetric feature splitting. Extensive experiments on the Chan Zuckerberg Initiative Imaging (CZII) cryo-ET dataset demonstrate that our method significantly outperforms existing state-of-the-art approaches, including DeepFinder, standard UNet3D, YOLOv5-3D, and 3D ResNet models, achieving a mean recall of 0.8848 and F4-score of 0.7969. The framework demonstrates robust performance across various protein particle types and imaging conditions, offering a promising technical solution for high-throughput structural biology workflows requiring accurate macromolecular annotation in cellular cryo-ET data.