Brain Tumor Segmentation by Variability Characterization of Tumor Boundaries
Automated medical image analysis can play an important role in diagnoses and treatment assessment, but integration and interpretation across heterogeneous data sources remain significant challenges. In particular, automated estimation of tumor extent in glioblastoma patients has been challenging given the diversity of tumor shapes and appearance characteristics due to differences in magnetic resonance (MR) imaging acquisition parameters, scanner variations and heterogeneity in tumor biology. With this work, we present an approach for automated tumor segmentation using multimodal MR images. The algorithm considers the variability arising from the intrinsic tumor heterogeneity and segmentation error to derive the tumor boundary and produce an estimate of segmentation error. Using the MICCAI 2015 dataset, a Dice coefficient of 0.74 was obtained for whole tumor, 0.55 for tumor core, and 0.54 for active tumor, achieving above average performance in comparison to other approaches evaluated on the BRATS benchmark.
BrainLes@MICCAI