Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography
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Date
2020-11
Publication Type
Journal Article
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yes
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Abstract
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. © 2020 Elsevier B.V.
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published
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Volume
110
Pages / Article No.
101975
Publisher
Elsevier
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Subject
Mitral valve; Segmentation; Collaborative filtering; Neural network
Organisational unit
03659 - Buhmann, Joachim M. (emeritus) / Buhmann, Joachim M. (emeritus)
02803 - Collegium Helveticum / Collegium Helveticum