Regional Multi-View Learning for Cardiac Motion Analysis: Application to Identification of Dilated Cardiomyopathy Patients

Objective: The aim of this paper is to describe an automated diagnostic pipeline that uses as input only ultrasound (US) data, but is at the same time informed by a training database of multimodal magnetic resonance (MR) and US image data. Methods: We create a multimodal cardiac motion atlas from three-dimensional (3-D) MR and 3-D US data followed by multi-view machine learning algorithms to combine and extract the most meaningful cardiac descriptors for classification of dilated cardiomyopathy (DCM) patients using US data only. More specifically, we propose two algorithms based on multi-view linear discriminant analysis and multi-view Laplacian support vector machines (MvLapSVMs). Furthermore, a novel regional multi-view approach is proposed to exploit the regional relationships between the two modalities. Results: We evaluate our pipeline on the classification task of discriminating between normals and DCM patients. Results show that the use of multi-view classifiers together with a cardiac motion atlas results in a statistically significant improvement in accuracy compared to classification without the multimodal atlas. MvLapSVM was able to achieve the highest accuracy for both the global approach (92.71%) and the regional approach (94.32%). Conclusion: Our work represents an important contribution to the understanding of cardiac motion, which is an important aid in the quantification of the contractility and function of the left ventricular myocardium. Significance: The intended workflow of the developed pipeline is to make use of the prior knowledge from the multimodal atlas to enable robust extraction of indicators from 3-D US images for detecting DCM patients.