The morphology of the mitral annulus plays an important role in diagnosing and treating mitral valve disorders. Automated segmentation has the promise to be time-saving and improve consistency in clinical practice. In the past years, segmentation has been dominated by methods based on deep learning. Deep learning-based segmentation methods have shown good results, but their consistency and robustness are still subjects of active research. In this work, we introduce a method that combines Graph Convolutional Networks with a 3D CNN model to integrate an anatomical shape template for the predictions. Our method leverages the feature extraction capability of CNN models to provide input features to the graph neural networks. The proposed method leverages strengths from a shape model approach with the strengths of deep learning. Further, we propose loss functions for the CNN designed to guide the graph model training. The CNN was trained with transfer learning, using a limited number of labeled transesophageal echocardiography volumes to adapt to the mitral annulus segmentation task. When comparing the segmentation of the mitral annulus achieved by the proposed method with the test set annotations, the method showed a high degree of accuracy, achieving a curve-to-curve error of 2.0 +/- 0.81 mm and a relative perimeter error of 4.42 +/-3.33 %. Our results show that the proposed method is a promising new approach for introducing anatomical template structures in medical segmentation tasks.

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