  Tubular structure segmentation in medical images, e.g., segmenting vessels in
CT scans, serves as a vital step in the use of computers to aid in screening
early stages of related diseases. But automatic tubular structure segmentation
in CT scans is a challenging problem, due to issues such as poor contrast,
noise and complicated background. A tubular structure usually has a
cylinder-like shape which can be well represented by its skeleton and
cross-sectional radii (scales). Inspired by this, we propose a geometry-aware
tubular structure segmentation method, Deep Distance Transform (DDT), which
combines intuitions from the classical distance transform for skeletonization
and modern deep segmentation networks. DDT first learns a multi-task network to
predict a segmentation mask for a tubular structure and a distance map. Each
value in the map represents the distance from each tubular structure voxel to
the tubular structure surface. Then the segmentation mask is refined by
leveraging the shape prior reconstructed from the distance map. We apply our
DDT on six medical image datasets. The experiments show that (1) DDT can boost
tubular structure segmentation performance significantly (e.g., over 13%
improvement measured by DSC for pancreatic duct segmentation), and (2) DDT
additionally provides a geometrical measurement for a tubular structure, which
is important for clinical diagnosis (e.g., the cross-sectional scale of a
pancreatic duct can be an indicator for pancreatic cancer).
