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Dual-force convolutional neural networks for accurate brain tumor segmentation

Shengcong Chen,Changxing Ding,Minfeng Liu

Published 2019 in Pattern Recognition

ABSTRACT

Abstract Brain tumor segmentation from Magnetic Resonance Imaging scans is vital for both the diagnosis and treatment of brain cancers. It is widely accepted that accurate segmentation depends on multi-level information. However, exiting deep architectures for brain tumor segmentation fail to explicitly encourage the models to learn high-quality hierarchical features. In this paper, we propose a series of approaches to enhance the quality of the learnt hierarchical features. Our contributions incorporate four aspects. First, we extend the popular DeepMedic model to Multi-Level DeepMedic to make use of multi-level information for more accurate segmentation. Second, we propose a novel dual-force training scheme to promote the quality of multi-level features learnt from deep models. It is a general training scheme and can be applied to many exiting architectures, e.g., DeepMedic and U-Net. Third, we design a label distribution-based loss function as an auxiliary classifier to encourage the high-level layers of deep models to learn more abstract information. Finally, we propose a novel Multi-Layer Perceptron-based post-processing approach to refine the prediction results of deep models. Extensive experiments are conducted on two most recent brain tumor segmentation datasets, i.e., BRATS 2017 and BRATS 2015 datasets. Results on the two databases indicate that the proposed approaches consistently promote the segmentation performance of the two popular deep models.

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