DEEP LEARNING MEETS RADIOMICS FOR END-TO-END BRAIN TUMOR MRI ANALYSIS

Magnetic resonance imaging is the modality of choice in brain tumors to diagnose and monitor the patients with such lesions. However, its manual analysis is user-dependent and cumbersome, and is affected by significant intra- and inter-rater variability which hampers the objective assessment of the progression of the disease. In this work, we tackle this issue and introduce an end-to-end pipeline to not only segment the brain tumors in a fully-automated and reproducible way and calculate their volumetric characteristics, but also to benefit from radiomic features extracted from the tumorous tissue for classifying the scan as low- or high-grade gliomas. Our extensive experiments revealed that the automatically calculated volumetric measurements are in almost perfect agreement with human readers, and that the ensemble classifiers utilizing radiomic features extracted from whole-tumor areas deliver high-quality classification for a range of segmentation approaches. To make the experiments fully reproducible and to encourage other research groups to exploit our end-to-end pipeline, we made the implementation open-sourced.