  When diagnosing the brain tumor, doctors usually make a diagnosis by
observing multimodal brain images from the axial view, the coronal view and the
sagittal view, respectively. And then they make a comprehensive decision to
confirm the brain tumor based on the information obtained from multi-views.
Inspired by this diagnosing process and in order to further utilize the 3D
information hidden in the dataset, this paper proposes a multi-view dynamic
fusion framework to improve the performance of brain tumor segmentation. The
proposed framework consists of 1) a multi-view deep neural network
architecture, which represents multi learning networks for segmenting the brain
tumor from different views and each deep neural network corresponds to
multi-modal brain images from one single view and 2) the dynamic decision
fusion method, which is mainly used to fuse segmentation results from
multi-views as an integrate one and two different fusion methods, the voting
method and the weighted averaging method, have been adopted to evaluate the
fusing process. Moreover, the multi-view fusion loss, which consists of the
segmentation loss, the transition loss and the decision loss, is proposed to
facilitate the training process of multi-view learning networks so as to keep
the consistency of appearance and space, not only in the process of fusing
segmentation results, but also in the process of training the learning network.
\par By evaluating the proposed framework on BRATS 2015 and BRATS 2018, it can
be found that the fusion results from multi-views achieve a better performance
than the segmentation result from the single view and the effectiveness of
proposed multi-view fusion loss has also been proved. Moreover, the proposed
framework achieves a better segmentation performance and a higher efficiency
compared to other counterpart methods.
