广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (06): 168-175.doi: 10.12052/gdutxb.230131
曾安1, 陈旭宙1, 姬玉柱1, 潘丹2, 徐小维3
Zeng An1, Chen Xu-zhou1, Ji Yu-Zhu1, Pan Dan2, Xu Xiao-Wei3
摘要: 心脏多类分割在医学影像领域具有重要意义,可提供精准心脏结构信息,辅助临床诊断。然而,在高分辨率心脏影像多类语义分割模型的训练中,多次下采样导致深层特征的丢失,从而引发分割出来的心脏影像器官不连续和边缘分割错误等问题。为了应对这一挑战,本文提出基于自注意力和三维卷积的神经网络——3DCSNet。具体地,在网络中引入三维特征融合模块和三维空间感知模块,前者集成了自注意力和三维卷积并行特征提取,能够有效地分配特征图同一维度下的通道内部和通道之间的权重;后者通过融合自注意力机制,捕捉不同维度之间的位置相关性信息,避免因为下采样导致重要信息丢失,进一步保留深层关键特征。3DCSNet在公开的先天性心脏病三维计算机断层图像数据集(ImageCHD)上优于多个现有模型。
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