Multiple dynamic models for tracking the left ventricle of the heart from ultrasound data using particle filters and deep learning architectures

The problem of automatic tracking and segmentation of the left ventricle (LV) of the heart from ultrasound images can be formulated with an algorithm that computes the expected segmentation value in the current time step given all previous and current observations using a filtering distribution. This filtering distribution depends on the observation and transition models, and since it is hard to compute the expected value using the whole parameter space of segmentations, one has to resort to Monte Carlo sampling techniques to compute the expected segmentation parameters. Generally, it is straightforward to compute probability values using the filtering distribution, but it is hard to sample from it, which indicates the need to use a proposal distribution to provide an easier sampling method. In order to be useful, this proposal distribution must be carefully designed to represent a reasonable approximation for the filtering distribution. In this paper, we introduce a new LV tracking and segmentation algorithm based on the method described above, where our contributions are focused on a new transition and observation models, and a new proposal distribution. Our tracking and segmentation algorithm achieves better overall results on a previously tested dataset used as a benchmark by the current state-of-the-art tracking algorithms of the left ventricle of the heart from ultrasound images.