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Pituitary Adenoma Volumetry with 3D Slicer

Institution:
1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Neurosurgery, University Hospital of Marburg, Marburg, Germany.
Publisher:
Public Library of Science
Publication Date:
Dec-2012
Journal:
PLoS One.
Volume Number:
7
Issue Number:
12
Pages:
e51788
Citation:
PLoS One. 2012 Dec;7(12):e51788.
Links:
http://dx.doi.org/10.1371/journal.pone.0051788
PubMed ID:
23240062
PMCID:
PMC3519899
Appears in Collections:
NCIGT, NA-MIC, NAC, SLICER, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR019703/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Egger J., Kapur T., Nimsky C., Kikinis R. Pituitary Adenoma Volumetry with 3D Slicer. PLoS One. 2012 Dec;7(12):e51788. PMID: 23240062. PMCID: PMC3519899.
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In this study, we present pituitary adenoma volumetry using the free and open source medical image computing platform for biomedical research: (3D) Slicer. Volumetric changes in cerebral pathologies like pituitary adenomas are a critical factor in treatment decisions by physicians and in general the volume is acquired manually. Therefore, manual slice-by-slice segmentations in magnetic resonance imaging (MRI) data, which have been obtained at regular intervals, are performed. In contrast to this manual time consuming slice-by-slice segmentation process Slicer is an alternative which can be significantly faster and less user intensive. In this contribution, we compare pure manual segmentations of ten pituitary adenomas with semi-automatic segmentations under Slicer. Thus, physicians drew the boundaries completely manually on a slice-by-slice basis and performed a Slicer-enhanced segmentation using the competitive region-growing based module of Slicer named GrowCut. Results showed that the time and user effort required for GrowCut-based segmentations were on average about thirty percent less than the pure manual segmentations. Furthermore, we calculated the Dice Similarity Coefficient (DSC) between the manual and the Slicer-based segmentations to proof that the two are comparable yielding an average DSC of 81.9763.39%.

Additional Material
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Egger-pone.0051788-2012-fig1.jpg (229.142kB)