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The BraTS dataset contains a mixture of high-grade and low-grade gliomas,which have a rather different appearance: previous studies have shown thatperformance can be improved by separated training on low-grade gliomas (LGGs)and high-grade gliomas (HGGs), but in practice this information is notavailable at test time to decide which model to use. By contrast with HGGs,LGGs often present no sharp boundary between the tumor core and the surroundingedema, but rather a gradual reduction of tumor-cell density.  Utilizing our 3D-to-2D fully convolutional architecture, DeepSCAN, whichranked highly in the 2019 BraTS challenge and was trained using anuncertainty-aware loss, we separate cases into those with a confidentlysegmented core, and those with a vaguely segmented or missing core. Since byassumption every tumor has a core, we reduce the threshold for classificationof core tissue in those cases where the core, as segmented by the classifier,is vaguely defined or missing.  We then predict survival of high-grade glioma patients using a fusion oflinear regression and random forest classification, based on age, number ofdistinct tumor components, and number of distinct tumor cores.  We present results on the validation dataset of the Multimodal Brain TumorSegmentation Challenge 2020 (segmentation and uncertainty challenge), and onthe testing set, where the method achieved 4th place in Segmentation, 1st placein uncertainty estimation, and 1st place in Survival prediction.

Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty