Manual labeling strategy for ground truth estimation in MRI glial tumor segmentation

In this paper we focused our attention on the problem of de- termining reliable ground truth for validating unsupervised, fully automatic MRI brain tumor segmentation procedures in the clinical context of Glial Tumor treatment . The goal was achieved by proposing an integrated "visual knowledge elicitation strategy" centered on the use of GliMAn(Glial Tumor Manual Annotator), a 3D MRI navigator that al- lows to view and manually labeling MRI volumes. As seen in our experimental context, the manual labeling process benets from the insertion of a software tool taylored on the experts visual and usability requirements.