近日，研究中心硕士生周正超的一篇论文“From Contrast-Driven Segmentation to Central Lumbar Spinal Stenosis Grading: A Comprehensive Multi-View Spinal MRI Image Analysis”被IEEE TRANSACTIONS ON MEDICAL IMAGING（JCR一区，影响因子9.8）录用。（论文链接地址：https://ieeexplore.ieee.org/document/11371395）

论文摘要：中央型腰椎管狭窄症（central lumbar spinal stenosis, CLSS）是一种高发的退行性脊柱疾病，严重影响了患者的生活质量。轴位与矢状位MRI图像提供了丰富的组织结构和病变信息，对于实现精准诊断至关重要。然而，现有基于MRI的诊断方法仍然存在病灶定位能力不足、跨视角对齐不充分、多视图MRI信息利用不充分以及面对患者个体差异时泛化能力有限等问题。为此，本文提出了一种基于多视角MRI图像融合的中央型腰椎管狭窄分级模型——ELSG-MF（Encompassing Lumbar Central Spinal Stenosis Grading Model via Multi-view MRI Image Fusion）。ELSG-MF由三个阶段构成：第一阶段通过对比驱动的一致性增强策略提取鲁棒的伪标签，引导Med-SAM对脊柱组织结构进行精确定位与分割；第二阶段设计了矢状位—轴位配对（Sagittal-Axial Pairing, SAP）算法，结合椎体与椎间盘的空间解剖关系，以实现矢状位与横断位图像之间的关联配对；第三阶段创新性地设计了多视角自适应融合（Multi-view Adaptive Fusion, M²AF）模块，实现了跨视角解剖特征间的自适应动态融合，增强了视角间上下文互补信息的提取，并有效提升了模型对狭窄程度细微变化的辨识能力。大量实验表明，所提出模型取得了0.8631的总体准确率、0.96的AUC以及0.8614的F1-score。上述结果表明，该模型相较于主流方法表现出显著优势，在分割与分级精度方面均达到更优水平，同时展现出良好的泛化能力和临床应用潜力。

Abstract：Central lumbar spinal stenosis, a prevalent degenerative spinal disorder, severely impacts the quality of life for those affected. Axial and sagittal MRI images offer diverse information on tissue structure and lesions, which is crucial for accurate diagnosis. However, MRI-based diagnostic approaches still have poor lesion localization, insufficient cross-view alignment, underutilization of multi-view MRI information, and limited generalization across patient variability. To address these problems, we proposed an Encompassing Lumbar Central Spinal Stenosis Grading Model via Multi-view MRI Image Fusion called ELSG-MF. ELSG-MF consists of three stages: the first stage utilizes the extraction of robust pseudo-labels through a contrast-driven consistency reinforcement technique to guide Med-SAM in localizing and segmenting spinal tissue components. The Sagittal-Axial Pairing (SAP) Algorithm was developed by stage2 to integrate the spatial anatomical relationship between the vertebral body and the intervertebral disc, facilitating the correlation pairing between sagittal and axial images. Stage3 subsequently innovated the multi-view Adaptive Fusion (M²AF) module, which enables adaptive dynamic fusion of anatomical features across views. M²AF enhances the extraction of contextual complementary information, and significantly improves the model’s capacity to detect subtle variations in the degree of narrowness. A series of studies show that our model achieves an overall accuracy of 0.8631, AUC of 0.96, and F1-score of 0.8614. These results indicate that our model substantially outperforms mainstream approaches, attaining superior segmentation and grading accuracy, exhibiting robust generalization and clinical application potential.