While there is a large class of Multiple-Target Tracking (MTT) problems for which batch processing is possible and desirable, batch MTT remains relatively unexplored in comparison to sequential approaches. In this paper, we give a principled probabilistic formalization of batch MTT in which we introduce two new, very general constraints that considerably help us in reaching the correct solution. First, we exploit the correlation between the appearance of a target and its motion. Second, entrances and departures of targets are encouraged to occur at the boundaries of the scene. We show how to implement these constraints in a formal and efficient manner. Our approach is applied to challenging 3-D biomedical imaging data where the number of targets is unknown and may vary, and numerous challenging tracking events occur. We demonstrate the ability of our model to simultaneously track the nuclei of over one hundred migrating neuron precursor cells in image stack series collected from a 2-photon microscope.