title: Segmentation of pituitary adenoma: a graph-based method vs. a balloon inflation method.
abstract: Among all abnormal growths inside the skull, the percentage of tumors in sellar region is approximately 10-15%, and the pituitary adenoma is the most common sellar lesion. A time-consuming process that can be shortened by using adequate algorithms is the manual segmentation of pituitary adenomas. In this contribution, two methods for pituitary adenoma segmentation in the human brain are presented and compared using magnetic resonance imaging (MRI) patient data from the clinical routine: Method A is a graph-based method that sets up a directed and weighted graph and performs a min-cut for optimal segmentation results: Method B is a balloon inflation method that uses balloon inflation forces to detect the pituitary adenoma boundaries. The ground truth of the pituitary adenoma boundaries - for the evaluation of the methods - are manually extracted by neurosurgeons. Comparison is done using the Dice Similarity Coefficient (DSC), a measure for spatial overlap of different segmentation results. The average DSC for all data sets is 77.5±4.5% for the graph-based method and 75.9±7.2% for the balloon inflation method showing no significant difference. The overall segmentation time of the implemented approaches was less than 4s - compared with a manual segmentation that took, on the average, 3.9±0.5min.
authors.affiliation: Department of Neurosurgery, University of Marburg, Marburg, Germany. egger@med.uni-marburg.de
authors.forename: Jan
authors.initials: J
authors.lastname: Egger
authors.forename: Dženan
authors.initials: D
authors.lastname: Zukić
authors.forename: Bernd
authors.initials: B
authors.lastname: Freisleben
authors.forename: Andreas
authors.initials: A
authors.lastname: Kolb
authors.forename: Christopher
authors.initials: C
authors.lastname: Nimsky
mesh_terms: D000236:Adenoma / Q000473:pathology*; D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D008953:Models, Anatomic; D010911:Pituitary Neoplasms / Q000473:pathology*