Multiple particle tracking in time-lapse synchrotron X-ray images using discriminative appearance and neighbouring topology learning

Abstract

Recent research has introduced a novel method of directly monitoring the effects of potential therapies for Cystic Fibrosis (CF) airway disease by quantifying mucociliary transit (MCT). In this method, micron-sized spherical particles are deposited into rodent airways, and synchrotron X-ray images are obtained to quantify the motion of the particles. However, the accurate tracking of these particles is challenging due to low contrast, image noise, and the presence of overlapping particles. Therefore, this paper proposes a novel method for detecting and tracking circular particles and measuring their dynamics. Accurate particle detection is achieved by applying a convolutional neural network (CNN). For robust multi-object tracking, this paper proposes a confidence model utilizing appearance and neighbouring topology learned by linear discriminant analysis. We also propose a detection recovery method using multi-frame association to restore the missed particles due to overlapping. The proposed method is tested with several different datasets and shows high levels of detection and tracking accuracy. Finally, by offering visual tracking analyses that display merging and splitting events, the proposed method can provide a better understanding of airway MCT behaviour.