Mitral Valve Leaflets Segmentation in Echocardiography using Convolutional Neural Networks

Eva Costa, Nelson Martins, Malik Saad Sultan, Diana Veiga, Manuel Ferreira, Sandra Mattos, Miguel Coimbra

Abstract: Rheumatic heart disease remains a major burden in the developing countries. The World Heart Federation proposed guidelines for the echocardiographic detection of the disease, in which the mitral leaflets’ morphology assessment is a key indicator. The drawback is that these guidelines are dependent on the clinician experience. To overcome this limitation, we propose an automatic segmentation of the mitral leaflets using a new method based on convolutional neural network, specifically the UNet architecture. The results indicate a median DICE coefficient of 0.74 in PLAX and 0.79 in A4C for the anterior mitral leaflet segmentation, while median DICE of 0.60 in PLAX and 0.69 A4C are met for the posterior leaflet. A visual evaluation of this segmentation approach by two cardiologists is in line with the numerical results. The false detection due to overestimation and artifacts remains an issue to be addressed in the future.

Author Affiliation: Neadvance - Machine Vision S.A.

Keywords: Semantic Segmentation, Ultrasound imaging, Mitral Valve