Small pulmonary vessel networks (arteriole and venule) provide a significant insight into understanding the alveolated
structure in the human acinus. However, automatic extraction of small pulmonary vessels is a challenge due to the
presence of abundant complexities in the networks. We thereby introduce a stochastic framework, a particle filter, to
track small vessels running inside alveolar walls in human acinus using synchrotron radiation micro CT (SRμCT)
images. We formulated vessel tracking using a non-linear sate space which captures both smoothness of the trajectories and intensity coherence along vessel orientations. In the particle filter scheme, we computed the proposal distribution by using the orientation distribution function (ODF), which is estimated as the combination of three different profiles; appearance, directional, and medialness profiles. To model the posterior distribution, we obtained voxels inside cylindrical tube which encapsulated a local vessel part. We constructed the prior distribution using the von Mises-Fisher (vMF) distribution on a unit sphere. At the same time, we detected branches of a vessel by analyzing the dominance of local vessel orientations through the vMF mean shift algorithm. Given a seed point, the method is able to locate the optimal vessel networks inside alveolar walls. Applying the method to the SRμCT images of the human lung acini, we demonstrate its potential usefulness to extract the trajectories of small pulmonary vessels running inside the alveolar walls.