Scientific Reports · 2013 · 256 citations · 29 references
MedicineOncologyRadiologyBiomedical ImagingDiagnostic ImagingTreatment PlanningImage SegmentationMagnetic Resonance ImagingMedical Image AnalysisMedical ImagingPatient SegmentationVolume Segmentation3D Image SegmentationDiceMedical Image SegmentationSegmentationSlice-based SegmentationTumor SegmentationTumor Volume EstimationVariability Analysis
Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer - a free platform for biomedical research - provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 ± 5.23% and a Hausdorff Distance of 2.32 ± 5.23 mm.
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3D Slicer as an image computing platform for the Quantitative Imaging Network
Andriy Fedorov, Reinhard Beichel, Jayashree Kalpathy–Cramer et al. · Magnetic Resonance Imaging · 2012 · 8.9K citations · Full text
Arts, Computer Science, Radiology +12
Carsten Rother, Vladimir Kolmogorov, Andrew Blake · ACM Transactions on Graphics · 2004 · 5.8K citations · Full text