山东大学学报 (工学版) ›› 2025, Vol. 55 ›› Issue (1): 66-76.doi: 10.6040/j.issn.1672-3961.0.2023.329
• 机器学习与数据挖掘 • 上一篇
李伟豪1,2,3,王苹苹1,2,3,许万博1,2,3,4,魏本征1,2,3*
LI Weihao1,2,3, WANG Pingping1,2,3, XU Wanbo1,2,3,4, WEI Benzheng1,2,3*
摘要: 为挖掘腰椎磁共振成像(magnetic resonance image, MRI)图像中多种模态信息的相关性、腰椎结构间的相互依赖关系、腰椎结构先验知识对腰椎精准分割和疾病辅助诊断的重要价值,提出一种结构先验引导的多模态信息融合分割算法。设计的多模态图像编码模块(multi-modality encoding module, MMEM)可同时对T1和T2加权图像做语义特征提取;跨模态体素融合模块(cross-modality voxel fusion module, CMVF)可在融合过程中为各模态图像特征自适应分配融合权重。根据腰椎内部各组织结构间的先验知识构建图模型,利用图卷积神经网络分割模块(graph convolutional networks segmentation module,GCNSM)实现图模型上的语义信息传播。采用多模态图像解码模块(multi-modality decoding module, MMDM)对特征图进行解码,实现对椎体及椎间盘的精准图像分割。对山东大学齐鲁医院德州医院采集的190组患者MRI数据集进行试验验证,所设计算法的平均骰子系数Dice、交并比IoU、95% Hausdorff距离HD95和平均对称表面距离ASSD分别为90.3%、82.31%、4.40 mm和1.21 mm,结果表明了算法的有效性及先进性。
中图分类号:
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