A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images.

Segmentation, the process of delineating boundaries and features within images, is a vital part of both the clinical assessment and the computational 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 MRIs of 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 min, making MITKats between 4 and 11 times faster than competing semi-automatic algorithms, while retaining similar accuracy. We conclude with remarks on the utility of segmentation for medical data analysis as well as its further challenges.