  This paper proposes a fully automated atlas-based pancreas segmentation
method from CT volumes utilizing atlas localization by regression forest and
atlas generation using blood vessel information. Previous probabilistic
atlas-based pancreas segmentation methods cannot deal with spatial variations
that are commonly found in the pancreas well. Also, shape variations are not
represented by an averaged atlas. We propose a fully automated pancreas
segmentation method that deals with two types of variations mentioned above.
The position and size of the pancreas is estimated using a regression forest
technique. After localization, a patient-specific probabilistic atlas is
generated based on a new image similarity that reflects the blood vessel
position and direction information around the pancreas. We segment it using the
EM algorithm with the atlas as prior followed by the graph-cut. In evaluation
results using 147 CT volumes, the Jaccard index and the Dice overlap of the
proposed method were 62.1% and 75.1%, respectively. Although we automated all
of the segmentation processes, segmentation results were superior to the other
state-of-the-art methods in the Dice overlap.
