Nearly automatic vessels segmentation using graph-based energy minimization

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We present a nearly automatic tool for the accurate segmentation
of vascular structures in volumetric CTA images. Its inputs are a start
and an end seed points inside the vessel. The two-step graph-based energy
minimization method starts by computing the weighted shortest path between
the vessel seed endpoints based on local image and seed intensities and
vessel path geometric characteristics. It then automatically defines a
Vessel Region Of Interest (VROI) from the shortest path and the estimated
vessel radius, and extracts the vessels boundaries by minimize the energy
on a corresponding graph cut.

We evaluate our method within the 2009 MICCAI 3D Segmentation Challenge for
Clinical Applications Workshop. Experimental results on the 46 carotid
bifurcations from clinical CTAs, compared to ground-truth genrated by
averaging three manual annotations,
yield an average symmetric surface distance of
0.83mm and a Dice similarity of 81.8%, with only three input seeds.
These results indicates that our method is easy to use, produces accurate
segmentations of vessels lumen, and is robust to intensity variations
inside the vessels, radius changes, bifurcations, and nearby anatomical
structures with similar intensity values.

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Categories: Level sets, Region growing, Segmentation
Keywords: Carotid arteries, CTA, Segmentation, graph-cut, Shortest path
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