U-Swing: An Adaptive U-Net and Swin Fusion for WB-MRI Whole Spine Bone Marrow Segmentation.

Abstract

Whole-body MRI (WB-MRI) is a non-invasive imaging technique offering comprehensive anatomical coverage and high-resolution contrast, ideal for evaluating multi-system diseases without ionizing radiation. Recent advancements in parallel imaging have enhanced its utility in oncology and non-oncology applications. WB-MRI is routinely used for cancer staging, including in multiple myeloma (MM), prostate, and colorectal cancer, as well as in evaluating cancer predisposition syndromes and inflammatory conditions. In MM, WB-MRI is crucial for assessing bone marrow involvement and monitoring treatment response. However, manual analysis of WB-MRI for bone marrow (BM) diseases is time-consuming and prone to data loss, limiting its clinical utility. Tumor load in MM is spatially heterogeneous, requiring detailed BM feature extraction-such as size, volume, intensity, and texture-across the entire bone marrow space. Current guidelines, including Myeloma Response Assessment and Diagnosis System (MY-RADS), offer limited interpretation analysis, and automated methods for comprehensive BM characterisation remain underexplored. These goals rely on automated BM segmentation as a foundational step. This study introduces U-Swing, a hybrid deep learning model combining Swin Transformer (SM) and U-Net Modules (UM) designed for WB-MRI whole spine bone marrow segmentation. U-Swing incorporates dynamic feature fusion of the SMs and UMs via U-Swing Patch Fusion and hierarchical optimization through Stage-Wise U-Swing Adaptation (SUA). The model demonstrated superior performance in WB-MRI bone marrow segmentation using T1-weighted turbo spin-echo (T1W-TSE) sequences, achieving a Dice Similarity (DS) score of 0.928, a Hausdorff Distance (HD95) of 3.919 mm, and an Average Symmetric Surface Distance (ASSD) of 0.281 mm, outperforming model architectures such as U-Net, Swin-UNETR, and UNETR.