Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography
Andreassen, Veronesi, Gerard, Solberg, Samset (2020) Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography IEEE J Biomed Health Inform (IF: 7.7) 24(4) 994-1003Abstract
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.
Links
http://www.ncbi.nlm.nih.gov/pubmed/31831455http://dx.doi.org/10.1109/JBHI.2019.2959430
