MRPMedical Research and Practice2993-96902993-9704Art and Design10.61369/MRP.2025100011Article基于深度学习的骶髂关节炎CT图像识别方法https://artdesignp.com/journal/MRP/3/10/10.61369/MRP.2025100011张雷20253102025-10-20CT作为诊断骶髂关节炎病发的重要方式，依赖人工判读，导致误诊和漏诊。为此，本研究提出一种融合2.5D切片输入、多尺度特征提取与融合的深度学习模型MS-2.5D-Net。首先，选取包含骶髂关节中下段关节间隙的连续5层CT切片，构建2.5D输入，在保留三维上下文关联性的同时，降低计算复杂度；其次，修改模型首层卷积适配2.5D输入，在编码阶段嵌入空洞空间金字塔池化（ASPP）模块，同步提取关节间隙的局部微结构特征与全局形态学特征；最后，引入残差特征金字塔（RFPN）跨层融合高层语义与低层细节特征，缓解深层网络梯度消失问题。在29例样本数据集上的实验表明，该模型的敏感度为91.3%，特异性为95.2%，能有效应对骶髂关节炎判别难题，为临床辅助诊断提供了可靠的技术方案。骶髂关节炎,骨盆CT,2.5D切片,多尺度特征融合,深度学习

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