  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
expert-delineated 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.
