Reconstructing cerebrovascular networks under local physiological constraints by integer programming
A probabilistic approach to vessel network extraction. The maximum a posteriori estimate is efficiently computed by integer programming. The approach enforces physiological constraints on the vessel structure. Physiological properties are learned from high-resolution corrosion cast. Physiologically plausible networks are extracted from in-vivo datasets.
cerebrovascular networks integer programming vascular network extraction vessel segmentation vessel tracking