Iterated Residual Graph Convolutional Neural Network for Personalized Three-Dimensional Reconstruction of Left Myocardium from Cardiac MR Images.
Iterated Residual Graph Convolutional Neural Network for Personalized Three-Dimensional Reconstruction of Left Myocardium from Cardiac MR Images.
复制标题
DOI：
10.3390/s23177430
复制
发表时间：
2023-08-25
期刊：
影响因子：
--
通讯作者：
Niu Y
中科院分区：
文献类型：
--
作者：
Wang X;Yuan Y;Liu M;Niu Y
左室心肌三维重建对心脏疾病的诊断和治疗具有重要意义。本文提出了一种个性化的左心肌三维重建算法，使用心脏MR图像，结合残差图卷积神经网络。使用基于模型的算法重建的网格的精度在很大程度上受目标对象与平均模型之间的相似性的影响。初始三角形网格直接从左心肌的分割结果获得。然后使用迭代残差图卷积神经网络对网格进行变形。建立了顶点特征学习模块，采用编码器-解码器神经网络表示不同感受野的左心肌骨架，辅助网格变形。以这种方式，左心肌的形状和局部关系用于引导网格变形。对心脏MR图像进行了定性和定量对比实验，结果验证了所提出的方法与相关的最先进的方法相比的合理性和竞争力。
Three-dimensional reconstruction of the left myocardium is of great significance for the diagnosis and treatment of cardiac diseases. This paper proposes a personalized 3D reconstruction algorithm for the left myocardium using cardiac MR images by incorporating a residual graph convolutional neural network. The accuracy of the mesh, reconstructed using the model-based algorithm, is largely affected by the similarity between the target object and the average model. The initial triangular mesh is obtained directly from the segmentation result of the left myocardium. The mesh is then deformed using an iterated residual graph convolutional neural network. A vertex feature learning module is also built to assist the mesh deformation by adopting an encoder–decoder neural network to represent the skeleton of the left myocardium at different receptive fields. In this way, the shape and local relationships of the left myocardium are used to guide the mesh deformation. Qualitative and quantitative comparative experiments were conducted on cardiac MR images, and the results verified the rationale and competitiveness of the proposed method compared to related state-of-the-art approaches.
登录
查看更多内容
影响因子：
5.3
作者：
Ullah, Ihsan;Manzo, Mario;Madden, Michael G.
通讯作者：
Madden, Michael G.
影响因子：
11.8
作者：
Fang, Longwei;Wang, Zuowei;He, Huiguang
通讯作者：
He, Huiguang
基于 HRBF 的近似解表面重建的闭式表述
影响因子：
4.3
作者：
Liu, Shengjun;Wang, Charlie C. L.;Wang, Jun
通讯作者：
Wang, Jun
DOI：
10.1109/34.927467
发表时间：
2001-06-01
影响因子：
23.6
作者：
Cootes, TF;Edwards, GJ;Taylor, CJ
通讯作者：
Taylor, CJ
影响因子：
10.9
作者：
Gonzales, Matthew J.;Sturgeon, Gregory;Krishnamurthy, Adarsh;Hake, Johan;Jonas, Rene;Stark, Paul;Rappel, Wouter-Jan;Narayan, Sanjiv M.;Zhang, Yongjie;Segars, W. Paul;McCulloch, Andrew D.
通讯作者：
McCulloch, Andrew D.