  Vasculature is known to be of key biological significance, especially in the
study of cancer. As such, considerable effort has been focused on the automated
measurement and analysis of vasculature in medical and pre-clinical images. In
tumors in particular, the vascular networks may be extremely irregular and the
appearance of the individual vessels may not conform to classical descriptions
of vascular appearance. Typically, vessels are extracted by either a
segmentation and thinning pipeline, or by direct tracking. Neither of these
methods are well suited to microscopy images of tumor vasculature. In order to
address this we propose a method to directly extract a medial representation of
the vessels using Convolutional Neural Networks. We then show that these
two-dimensional centerlines can be meaningfully extended into 3D in anisotropic
and complex microscopy images using the recently popularized Convolutional Long
Short-Term Memory units (ConvLSTM). We demonstrate the effectiveness of this
hybrid convolutional-recurrent architecture over both 2D and 3D convolutional
comparators.
