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The segmentation of the mitral valve annulus and leaflets specifies a crucialfirst step to establish a machine learning pipeline that can support physiciansin performing multiple tasks, e.g.\ diagnosis of mitral valve diseases,surgical planning, and intraoperative procedures. Current methods for mitralvalve segmentation on 2D echocardiography videos require extensive interactionwith annotators and perform poorly on low-quality and noisy videos. We proposean automated and unsupervised method for the mitral valve segmentation based ona low dimensional embedding of the echocardiography videos using neural networkcollaborative filtering. The method is evaluated in a collection ofechocardiography videos of patients with a variety of mitral valve diseases,and additionally on an independent test cohort. It outperforms state-of-the-art\emph{unsupervised} and \emph{supervised} methods on low-quality videos or inthe case of sparse annotation.

Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography