Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks

Muhammad Imran RAZZAK, Muhammad IMRAN, Guandong XU

Research output: Contribution to journalArticlespeer-review

134 Citations (Scopus)


Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive. Copyright © 2013 IEEE.

Original languageEnglish
Pages (from-to)1911-1919
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
Early online dateOct 2018
Publication statusPublished - Sept 2019


Razzak, M. I., Imran, M., & Xu, G. (2018). Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE Journal of Biomedical and Health Informatics, 23(5), 1911-1919. https://doi.org/10.1109/JBHI.2018.2874033


  • Brain tumor
  • Group CNN
  • CNN
  • Deep neural network
  • Group convolutional neural networks
  • Cascade CNN
  • Two-pathway CNN
  • 2PG-CNN


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