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The segmentation of thrombus and vessel in microscopic image sequences is of high interest for identifying genes linked to cardiovascular diseases. This task is however challenging because of the low contrast and the highly dynamic conditions observed in time-lapse DIC in-vivo microscopic scenes. In this work, we introduce a probabilistic framework for the joint segmentation of thrombus and vessel regions. Modeling the scene with dynamic textures, we derive two likelihood functions to account for both spatial and temporal discrepancies of the motion patterns. A tubular shape prior is moreover introduced to constrain the aortic region. Extensive experiments on microscopic sequences quantitatively show the good performance of our approach.

Joint Thrombus and Vessel Segmentation Using Dynamic Texture Likelihoods and Shape Prior