  The most common sellar lesion is the pituitary adenoma, and sellar tumors are
approximately 10-15% of all intracranial neoplasms. Manual slice-by-slice
segmentation takes quite some time that can be reduced by using the appropriate
algorithms. In this contribution, we present a segmentation method for
pituitary adenoma. The method is based on an algorithm that we have applied
recently to segmenting glioblastoma multiforme. A modification of this scheme
is used for adenoma segmentation that is much harder to perform, due to lack of
contrast-enhanced boundaries. In our experimental evaluation, neurosurgeons
performed manual slice-by-slice segmentation of ten magnetic resonance imaging
(MRI) cases. The segmentations were compared to the segmentation results of the
proposed method using the Dice Similarity Coefficient (DSC). The average DSC
for all datasets was 75.92% +/- 7.24%. A manual segmentation took about four
minutes and our algorithm required about one second.
