山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (2): 80-89.doi: 10.6040/j.issn.1672-3961.0.2023.021
• 机器学习与数据挖掘 • 上一篇
高泽文1,2,王建3,魏本征1,2*
GAO Zewen1,2, WANG Jian3, WEI Benzheng1,2*
摘要： 为提高脑胶质瘤核磁共振成像(magnetic resonance imaging, MRI)图像分割精度及质量,设计一种混合偏移轴向自注意力机制的脑胶质瘤分割多层级轴向注意力网络(multi-level axial-attention net, MLA-Net )算法。MLA-Net算法框架中设计的混合偏移轴向自注意力机制和混和损失函数,分别用于提取更精确的全局相对位置关系、提升网络对细节结构特征的敏感程度和实现精确地分割胶质瘤模糊边界。试验结果表明,在BraTS 2018和2019的混合数据上,MLA-Net算法的dice系数可达到0.843 3, Hausdorff距离为2.587。MLA-Net算法的MRI图像脑胶质分割性能优良,可以融合全局相对位置特征和局部细节特征,更好地分割出脑胶质瘤感兴趣区域。
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