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<p>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&#xa0;mm, and an Average Symmetric Surface Distance (ASSD) of 0.281&#xa0;mm, outperforming model architectures such as U-Net, Swin-UNETR, and UNETR.</p>

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

  • 期刊论文

  • 发表日期：

    2025年10月7日

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  • George G. Botis,
  • Theodoros Panagiotis Vagenas,
  • Nikolas Robotis,
  • Vassilis Koutoulidis,
  • Lia A. Moulopoulos,
  • George K. Matsopoulos

摘要

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.