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|Title:||Pituitary Adenoma Volumetry with 3D Slicer|
|Authors:||Egger, Jan;Kapur, Tina;Nimsky, Christopher;Kikinis, Ron|
|subject:||Biology;Endocrine System;Endocrine Physiology;Pituitary;Computer Science;Algorithms;Computing Methods;Medicine;Oncology;Endocrine Tumors;Pituitaryadenomas;Radiology;Anatomy;Physiology;Cancers;Neoplasms|
|Publisher:||Public Library of Science|
|Description:||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 81.97±3.39%.|
|Standard no:||Egger, Jan, Tina Kapur, Christopher Nimsky, and Ron Kikinis. 2012. Pituitary adenoma volumetry with 3D slicer. PLoS ONE 7(12): e51788.|
|Appears in Collections:||HMS Scholarly Articles|
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