The model cascade strategy that runs a series of deep models sequentially for coarse-to-fine medical image segmentation is becoming increasingly popular, as it effectively relieves the class imbalance problem. This strategy has achieved state-of-the-art performance in many segmentation applications but results in undesired system complexity and ignores correlation among deep models. In this paper, we propose a light and clean deep model that conducts brain tumor segmentation in a single-pass and solves the class imbalance problem better than model cascade. First, we decompose brain tumor segmentation into three different but related tasks and propose a multi-task deep model that trains them together to exploit their underlying correlation. Second, we design a curriculum learning-based training strategy that trains the above multitask model more effectively. Third, we introduce a simple yet effective post-processing method that can further improve the segmentation performance significantly. The proposed methods are extensively evaluated on BRATS 2017 and BRATS 2015 datasets, ranking first on the BRATS 2015 test set and showing top performance among 60+ competing teams on the BRATS 2017 validation set.
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