Segmentation, the process of delineating tumor apart from healthy tissue, is a vital part of both the clinical assessment and the quantitative analysis of brain cancers. Here, we provide an open-source algorithm (MITKats), built on the Medical Imaging Interaction Toolkit, to provide user-friendly and expedient tools for semi-automatic segmentation. To evaluate its performance against competing algorithms, we applied MITKats to 38 high-grade glioma cases from publicly available benchmarks. The similarity of the segmentations to expertdelineated ground truths approached the discrepancies among different manual raters, the theoretically maximal precision. The average time spent on each segmentation was 5 minutes, making MITKats between 4 and 11 times faster than competing semi-automatic algorithms, while retaining similar accuracy.