Left Ventricle Landmark Localization and Identification in Cardiac MRI by Deep Metric Learning-Assisted CNN Regression

Publication date: Available online 21 February 2020Source: NeurocomputingAuthor(s): Xuchu Wang, Suiqiang Zhai, Yanmin NiuAbstractAccurate 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 Cardiac Atlas Project (CAP) data set, and experimenta...

Source: Neurocomputing - February 23, 2020 Category: Neuroscience Source Type: research