Machine learning based image segmentation relies on having access to a large dataset of labeled scans. Challenges arise when a sufficient training dataset is not available. To build a labeled dataset, one can manually create segmentations either by hand or assisted by a semi-automatic interactive tool. Here, interaction is given by the user in form of foreground and background clicks on the image. This thesis evaluates semi-automatic interactive image segmentation models applied to 3D brain tumor segmentation from MRI scans under two conditions. The first condition involves training models on the entire dataset. This provides a baseline for the best expected performance for each model. The second condition involves training models on portions of the dataset. This is done in an effort to model a dataset being gradually created from a small dataset. In both conditions, we find that the Wilcoxon signed rank test indicates significant results when comparing some of our interactive models with their fully-automatic counterpart. However, we ultimately deem the difference to be clinically irrelevant.