Automatic Segmentation of Brain Tumor Using 3D SE-Inception Networks with Residual Connections

  • Hongdou Yao
  • Xiaobing ZhouEmail author
  • Xuejie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)


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.


3D-SE-Inception-ResNet Cascaded FCN Anisotropic Medical image segmentation 



We’d like to thank the team of Wang [22] for their source code, with which we can focus more on the innovation of the model and do our work more easily. And we also thank the NiftyNet team, the deep learning tools they developed enables us construct our model more efficiently. This work was supported by the Natural Science Foundations of China under Grants No. 61463050, No. 61702443, No. 61762091, the NSF of Yunnan Province under Grant No. 2015FB113, the Project of Innovative Research Team of Yunnan Province under Grant No. 2018HC019.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYunnan UniversityKunmingPeople’s Republic of China

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