A fully automatic deep learning method for mitral annulus segmentation in 3D transesophageal echocardiography achieved a mean error of 2.0 mm (SD 1.9 mm).
Does a deep learning-based fully automatic method accurately segment the mitral annulus in 3D transesophageal echocardiography?
A fully automatic deep learning method can accurately segment the mitral annulus in 3D TEE, potentially eliminating inter-observer variability and manual interaction.
3D Transesophageal Echocardiography is an excellent tool for evaluating the mitral valve and is also well suited for guiding cardiac interventions. We introduce a fully automatic method for mitral annulus segmentation in 3D Transesophageal Echocardiography, which requires no manual input. One hundred eleven multi-frame 3D transesophageal echocardiography recordings were split into training, validation, and test sets. Each 3D recording was decomposed into a set of 2D planes, exploiting the symmetry around the centerline of the left ventricle. A deep 2D convolutional neural network was trained to predict the mitral annulus coordinates, and the predictions from neighboring planes were regularized by enforcing continuity around the annulus. Applying the final model and post-processing to the test set data gave a mean error of 2.0 mm - with a standard deviation of 1.9 mm. Fully automatic segmentation of the mitral annulus can alleviate the need for manual interaction in the quantification of an array of mitral annular parameters and has the potential to eliminate inter-observer variability.
Andreassen et al. (Thu,) conducted a other in Mitral annulus segmentation (n=111). Deep 2D convolutional neural network was evaluated on Mean error of mitral annulus coordinates prediction. A fully automatic deep learning method for mitral annulus segmentation in 3D transesophageal echocardiography achieved a mean error of 2.0 mm (SD 1.9 mm).