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 unsupervised and supervised methods on low-quality videos or in the case of sparse annotation.

二尖瓣环和瓣叶的分割指定了建立机器学习管道的关键第一步，该管道可以支持医生执行多项任务，例如二尖瓣疾病的诊断、手术计划和术中程序。当前对 2D 超声心动图视频进行二尖瓣分割的方法需要与注释者进行广泛的交互，并且在低质量和嘈杂的视频上表现不佳。我们提出了一种基于使用神经网络协同过滤的超声心动图视频的低维嵌入的二尖瓣分割的自动化和无监督方法。该方法在一系列患有各种二尖瓣疾病的患者的超声心动图视频中进行了评估，此外还在一个独立的测试队列中进行了评估。它优于最先进的低质量视频或稀疏注释的无监督和监督方法。