Real-time mitral annulus segmentation from 4D transesophageal echocardiography using deep learning regression

Segmentation of the mitral annulus is an important step in many cardiac applications. Current methods to delineate the mitral annulus often require extensive user interaction. Several methods have been proposed to automate mitral annulus segmentation, but often use methods which require sampling 2D planes from the 3D volume, discarding some of the contextual information contained in the original 3D volume. We propose a new 4D mitral annulus segmentation method based on 3D CNN regression of Fourier coefficients describing the shape of predicted annulus. Our model predicts a set of ten coefficients for each of the three image axes, which can then be used to sample annulus coordinates through the inverse Fourier transform. We acquired a dataset of 90 cases from diagnostic imaging of mitral valve patients, with corresponding annulus segmentations. This was split into training, validation and test sets of 75, 5, and 10 cases respectively. Following training, our model achieves a curve-to-curve accuracy of 5.5 +/- 2.2 mm on the test set, with training accuracy of 0.46 +/- 0.21 mm. Our model achieves accuracy similar to current state-of-the-art methods, and can achieve inference speed of 40 frames-per-second, which is suitable for use in real-time image guidance applications.