  Sellar tumors are approximately 10-15% among all intracranial neoplasms. The
most common sellar lesion is the pituitary adenoma. Manual segmentation is a
time-consuming process that can be shortened by using adequate algorithms. In
this contribution, we present a segmentation method for pituitary adenoma. The
method is based on an algorithm we developed recently in previous work where
the novel segmentation scheme was successfully used for segmentation of
glioblastoma multiforme and provided an average Dice Similarity Coefficient
(DSC) of 77%. This scheme is used for automatic adenoma segmentation. In our
experimental evaluation, neurosurgeons with strong experiences in the treatment
of pituitary adenoma performed manual slice-by-slice segmentation of 10
magnetic resonance imaging (MRI) cases. Afterwards, the segmentations were
compared with the segmentation results of the proposed method via the DSC. The
average DSC for all data sets was 77.49% +/- 4.52%. Compared with a manual
segmentation that took, on the average, 3.91 +/- 0.54 minutes, the overall
segmentation in our implementation required less than 4 seconds.
