Cardiac Multiclass Segmentation Method Based on Self-attention and 3D Convolution

Zeng An; Chen Xu-zhou; Ji Yu-Zhu; Pan Dan; Xu Xiao-Wei

doi:10.12052/gdutxb.230131

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Cardiac Multiclass Segmentation Method Based on Self-attention and 3D Convolution

更新时间：2026-05-27

- Cardiac Multiclass Segmentation Method Based on Self-attention and 3D Convolution
- Vol. 40, Issue 6, Pages: 168-175(2023)
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 广东技术师范大学电子与信息学院,广东,广州,510665
  3. 广东省人民医院(广东省医学科学院) 心外科,广东,广州,510080
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230131
  CLC： TP391.4
- Online First：25 November 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
曾安, 陈旭宙, 姬玉柱, 潘丹, 徐小维. 基于自注意力和三维卷积的心脏多类分割方法[J]. 广东工业大学学报, 2023, 40(6): 168-175.

Zeng An, Chen Xu-zhou, Ji Yu-Zhu, et al. Cardiac Multiclass Segmentation Method Based on Self-attention and 3D Convolution[J]. 2023, 40(6): 168-175.
曾安, 陈旭宙, 姬玉柱, 潘丹, 徐小维. 基于自注意力和三维卷积的心脏多类分割方法[J]. 广东工业大学学报, 2023, 40(6): 168-175. DOI： 10.12052/gdutxb.230131.

Zeng An, Chen Xu-zhou, Ji Yu-Zhu, et al. Cardiac Multiclass Segmentation Method Based on Self-attention and 3D Convolution[J]. 2023, 40(6): 168-175. DOI： 10.12052/gdutxb.230131.

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。

Abstract

Cardiac multi-class segmentation is of great significance in medical imaging

which can provide accurate cardiac structure information and assist clinical diagnosis. However

in the training of multi-class semantic segmentation models with high-resolution cardiac images

the loss of deep features due to multiple downsampling operations leads to the problems oforgan discontinuity and incorrect edge segmentation in the segmented cardiac. To address this

this paper proposes a 3DCSNet based on self-attention and 3D convolution for cardiac multi-class segmentation. Specifically

our proposed network introduces the 3D feature fusion module and a 3D spatial perception module into the segmentation network. The former 3D feature fusion module integrates self-attention and 3D convolution for parallel feature extraction

which is able to efficiently allocate the attentions weights within and between channels under the same dimension of the feature map. The latter 3D spatial perception module captures the positional correlation information between different dimensions by integrating the self-attention mechanism

avoiding the loss of important information in downsampling and further retaining the deep key features. Experimental results show that the proposed 3DCSNet outperforms several existing models on a publicly available 3D computed tomography image dataset (ImageCHD) .

关键词

Keywords

references

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