Tracking of microtubules in anisotropic volumes of neural tissue

For both the automatic and manual reconstruction of neural circuits from electron microscopy (EM) images, the detection and identification of intracellular structures provide useful cues. This is particularly true for microtubules which are indicative of the scaffold of neuronal morphology. However, to our knowledge, the automated reconstruction of microtubules from EM images of neural tissue has received no attention so far. In this paper, we present an automatic method for the tracking of microtubules in 3D EM volumes of neural tissue. We formulate an energy-based model on short candidate segments of microtubules found by a local classifier. We enumerate and score possible links between candidates, in order to find a cost-minimal subset of candidates and links by solving an integer linear program. The model provides a way to incorporate biological priors including both hard constraints (e.g. microtubules are topologically chains of links) and soft constraints (e.g. high curvature is unlikely). We test our method on a challenging EM dataset of Drosophila neural tissue and show that our model reliably tracks microtubules spanning many image sections.