Computer Science – Computational Engineering – Finance – and Science
Scientific paper
2011-03-09
Computer Science
Computational Engineering, Finance, and Science
4 pages, 5 figures, BIOSIGNAL, Berlin, 2010
Scientific paper
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.
Bauer Miriam H. A.
Egger Jan
Freisleben Bernd
Kuhnt Daniela
Nimsky Christopher
No associations
LandOfFree
Pituitary Adenoma Segmentation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Pituitary Adenoma Segmentation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Pituitary Adenoma Segmentation will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-644365