Refined query network (RQNet) for precise MRI segmentation and robust TED activity assessment

<i>Objective.</i>To develop an efficient deep learning framework for precise three-dimensional (3D) segmentation of complex orbital structures in multi-sequence magnetic resonance imaging (MRI) and robust assessment of thyroid eye disease (TED) activity, thereby addressing limitations in computational complexity, segmentation accuracy, and integration of multi-sequence features to support clinical decision-making.<i>Approach.</i>We propose RQNet, a U-shaped 3D segmentation network that incorporates the novel Refined Query Transformer Block with refined attention query multi-head self-attention. This design reduces attention complexity fromO(N2)toO(N⋅M)(M≪N) through pooled refined queries. High-quality segmentations then feed into a radiomics pipeline that extracts features per region of interest-including shape, first-order, and texture descriptors. The MRI features from the three sequences-T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1CE), and T2-weighted imaging (T2WI)-are subsequently integrated, with support vector machine, random forest, and logistic regression models employed for assessment to distinguish between active and inactive TED phases.<i>Main results.</i>RQNet achieved Dice similarity coefficients of 83.34%-87.15% on TED datasets (T1WI, T2WI, T1CE), outperforming state-of-the-art models such as nnFormer, UNETR, SwinUNETR, SegResNet, and nnUNet. The radiomics fusion pipeline yielded area under the curve values of 84.65%-85.89% for TED activity assessment, surpassing single-sequence baselines and confirming the benefits of multi-sequence MRI feature fusion enhancements.<i>Significance.</i>The proposed RQNet establishes an efficient segmentation network for 3D orbital MRI, providing accurate depictions of TED structures, robust radiomics-based activity assessment, and enhanced TED assessment through multi-sequence MRI feature integration.