Automatic segmentation of brain tumor using 3D SE-inception networks with residual connections

Abstract

Nowadays, there are various kinds of methods in medical image segmentation tasks, in which Cascaded FCN is an effective one. The idea of this method is to convert multiple classification tasks into a sequence of two categorization tasks, according to a series of sub-hierarchy regions of multi-modal Magnetic Resonance Images. We propose a model based on this idea, by combining the mainstream deep learning models for two dimensional images and modifying the 2D model to adapt to 3D medical image data set. Our model uses the Inception model, 3D Squeeze and Excitation structures, and dilated convolution filters, which are well known in 2D image segmentation tasks. When segmenting the whole tumor, we set the bounding box of the result, which is used to segment tumor core, and the bounding box of tumor core segmentation result will be used to segment enhancing tumor. We not only use the final output of the model, but also combine the results of intermediate output. In MICCAI BraTs 2018 gliomas segmentation task, we achieve a competitive performance without data augmentation.