Tracking Colliding Cells In Vivo Microscopy

Leukocyte motion represents an important component in the innate immune response to infection. Intravital microscopy is a powerful tool as it enables in vivo imaging of leukocyte motion. Under inflammatory conditions, leukocytes may exhibit various motion behaviors, such as flowing, rolling, and adhering. With many leukocytes moving at a wide range of speeds, collisions occur. These collisions result in abrupt changes in the motion and appearance of leukocytes. Manual analysis is tedious, error prone, time consuming, and could introduce technician-related bias. Automatic tracking is also challenging due to the noise inherent in in vivo images and abrupt changes in motion and appearance due to collision. This paper presents a method to automatically track multiple cells undergoing collisions by modeling the appearance and motion for each collision state and testing collision hypotheses of possible transitions between states. The tracking results are demonstrated using in vivo intravital microscopy image sequences. We demonstrate that 1) 71% of colliding cells are correctly tracked; (2) the improvement of the proposed method is enhanced when the duration of collision increases; and (3) given good detection results, the proposed method can correctly track 88% of colliding cells. The method minimizes the tracking failures under collisions and, therefore, allows more robust analysis in the study of leukocyte behaviors responding to inflammatory conditions.