We consider the problem of automatically tracking the mitral valve in cardiac ultrasound time series and present an unsupervised method for decomposing and segmenting the mitral valve from noisy ultrasound videos. To do so we propose a Robust Nonnegative Matrix Factorization (RNMF) method that naturally decomposes the time series into three separate parts, highlighting the cardiac cycle, mitral valve, and ultrasound noise. The low rank component of RNMF captures the simple motions of the cardiac cycle effectively aside from the sporadic motion of the mitral valve tissue that is captured innately in our RNMF sparse signal term. Using the RNMF representation, we introduce a simple valve object detection algorithm. Our method performs especially well in noisy time series when existing methods fail, differentiating general noise from the subtle and complex motions of the mitral valve. The valve is then segmented using simple thresholding and diffusion. The method presented is highly robust to low quality ultrasound video, and does not require manual preprocessing, prior labeling, or any training data.
We consider the problem of identifying frames in a cardiac ultrasound video associated with left ventricular chamber end-systolic (ES, contraction) and end-diastolic (ED, expansion) phases of the cardiac cycle. Our procedure involves a simple application of non-negative matrix factorization (NMF) to a series of frames of a video from a single patient. Rank-2 NMF is performed to compute two end-members. The end members are shown to be close representations of the actual heart morphology at the end of each phase of the heart function. Moreover, the entire time series can be represented as a linear combination of these two end-member states thus providing a very low dimensional representation of the time dynamics of the heart. Unlike previous work, our methods do not require any electrocardiogram (ECG) information in order to select the end-diastolic frame. Results are presented for a data set of 99 patients including both healthy and diseased examples.
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