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Egger, J; Kapur, T; Nimsky, C; Kikinis, R.
Pituitary adenoma volumetry with 3D Slicer.
PLoS One. 2012; 7(12):e51788-e51788 Doi: 10.1371/journal.pone.0051788 [OPEN ACCESS]
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Leading authors Med Uni Graz: Egger Jan
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Abstract:: 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%.
Find related publications in this database (using NLM MeSH Indexing): Algorithms -; Humans -; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Pituitary Neoplasms - diagnostic imaging; Pituitary Neoplasms - physiopathology; Radiography -; Tumor Burden -