Ihor Smal

TL;DR: A Bayesian approach is used to simultaneously track the locations of objects with different motion behaviors and the stage drift using image data obtained from fluorescence microscopy experiments.

TL;DR: This paper evaluates the performance of four frequently used concepts found in the literature for cardiac motion analysis in 2D tMRI image sequences, and proposes a new probabilistic method for tag tracking that serves as a complementary step to existing methods.

TL;DR: Experiments on synthetic images of three different types, as well as on real image data sets acquired for two different biological studies, revealed that for very low SNRs, the supervised (machine learning) methods perform best overall, closely followed by the detectors based on the so-called h-dome transform from mathematical morphology and the multiscale variance-stabilizing transform, which do not require a learning stage.

TL;DR: A deep-learning-based method that uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next is presented.

TL;DR: This paper reports on the development of a novel dynamic coronary roadmapping approach for improving visual feedback and reducing contrast use during PCI, and proposes a new deep learning based Bayesian filtering method that integrates the detection outcome of a convolutional neural network and the motion estimation between frames using a particle filtering framework.