Slicer manual#

The segmentation process is for both stages the same. Therefore, the data is divided into two pools of data sets: the training data set and the evaluation data set. The overall workflow of the proposed study is presented on the right side it starts with the image data and ends with the training or the evaluation process. During the evaluation phase the time for the initialization and the refinement has been measured. This results into the refinement phase where the Editor tools under Slicer are used to correct the automatic segmentation result – mostly by navigating along the axial slices. Then, the automatic segmentation is started and afterwards reviewed by the user. The segmentation process starts with the initialization of the GrowCut algorithm by the user on an axial, sagittal and coronal slice.

Slicer manual#

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. An open-source, feature-rich, frequently updated tool that contains everything you need to export the perfect print files for your 3D printer. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. 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. (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. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions.