Left ventricle landmark localization and identification in cardiac MRI by deep metric learning-assisted CNN regression
Left ventricle landmark localization and identification in cardiac MRI by deep metric learning-assisted CNN regression
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DOI：
10.1016/j.neucom.2020.02.069
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发表时间：
2020-07
期刊：
影响因子：
6
通讯作者：
Xuchu Wang;Suiqiang Zhai;Yanmin Niu
中科院分区：
文献类型：
--
作者：
Xuchu Wang;Suiqiang Zhai;Yanmin Niu
心脏MRI中左心室标志点的准确定位在心脏病的计算机辅助诊断中起着至关重要的作用。典型的分类模型仅利用图像的局部信息，难以处理伪影和界标区域的低区分度，而回归模型存在随机样本和标签之间的模糊性以及样本的不平衡性。为了克服这一局限性，本文提出了一种基于深度距离度量学习和CNN（卷积神经网络）回归的心脏MRI左心室标志点定位和识别方法。该方法包括样本生成和回归两个阶段，在样本生成阶段，结合超像素过分割和无监督深度度量学习提取局部图像的嵌入信息，然后将嵌入三元网络提取的特定超像素块和网格块集成，设计了双通道显著性样本挖掘模块。该三元组网络的权值被用于构建CNN回归模型，并且显著样本被用于预测地标坐标点云集群。通过Mean Shift迭代将每个点云簇的质心细化为界标。在CAP（Cardiac Atlas Project）数据集上对所提方法和相关方法进行了全面的评估，实验结果表明，所提方法达到了有竞争力的准确性，并且优于最先进的基于分类和回归的模型。
Accurate left ventricle landmark localization in cardiac MRI plays a vital role in computer-aided diagnosis of heart disease. Typical classification models hardly deal with artifacts and low discrimination of landmark regions by using only local image information, while regression models suffer from ambiguity between random samples and label, also the imbalance of samples. To overcome this limitation, this paper proposes a left ventricle landmark localization and identification method in cardiac MRI based on deep distance metric learning and CNN (convolutional neural network) regression. The method includes sample generation and regression stages, where super-pixel over-segmentation and unsupervised deep metric learning are integrated to extract the embedding information of local images, then a dual-channel salient sample mining module is designed by integrating specific super-pixel patches and grid patches extracted by the embedded triplet network. The weights of this triplet network are fed to build the CNN regression model, and the salient samples are employed to predict the landmark coordinate point cloud clusters. Furthermore, the centroid of each point cloud cluster is refined as landmark by Mean Shift iteration. The proposed method and close related methods were thoroughly evaluated on the CAP (cardiac atlas project) data set, and experimental results show that the proposed method achieves the competitive accuracy and outperforms the state-of-the-art classification and regression-based models.