Mitral Annulus Segmentation and Anatomical Orientation Detection in TEE Images Using Periodic 3D CNN

Abstract

Segmentation of the mitral annulus is often an important step in cardiac examinations. We propose a robust 3D method for predicting the anatomical orientation and segmentation of the mitral annulus in 3D transesophageal echocardiography. The method takes advantage of the circular anatomy of the annulus by utilizing cylinder coordinate samples and a 3D convolutional neural network with circular convolutions. Furthermore, the paper proposes new landmark detection loss functions based on the earth mover’s distance. The method’s effectiveness was demonstrated by training a HighRes3dNet model and evaluating its performance on a separate test set consisting of 135 frames from 19 examinations. The obtained coordinate prediction error was 1.96± 1.62 mm, and the anatomical orientation prediction error was 9.7° ± 15.8°. The robust and fully automatic mitral annulus segmentation and orientation prediction provided by the method can ease the workload of clinicians and provide time savings in clinics.

Keywords

• Deep learning
• earth mover’s distance
• echocardiography
• landmark detection
• mitral annulus segmentation