Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography
This addresses the problem of reducing manual annotation for physicians in diagnosing mitral valve diseases, though it is incremental as it builds on existing collaborative filtering techniques.
The paper tackled automated mitral valve segmentation in noisy echocardiography videos by proposing an unsupervised neural collaborative filtering method, which outperformed both unsupervised and supervised state-of-the-art methods on low-quality or sparsely annotated data.
The segmentation of the mitral valve annulus and leaflets specifies a crucial first step to establish a machine learning pipeline that can support physicians in performing multiple tasks, e.g.\ diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. Current methods for mitral valve segmentation on 2D echocardiography videos require extensive interaction with annotators and perform poorly on low-quality and noisy videos. We propose an automated and unsupervised method for the mitral valve segmentation based on a low dimensional embedding of the echocardiography videos using neural network collaborative filtering. The method is evaluated in a collection of echocardiography 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 in the case of sparse annotation.