  Delineating the brain tumor from magnetic resonance (MR) images is critical
for the treatment of gliomas. However, automatic delineation is challenging due
to the complex appearance and ambiguous outlines of tumors. Considering that
multi-modal MR images can reflect different tumor biological properties, we
develop a novel multi-modal tumor segmentation network (MMTSN) to robustly
segment brain tumors based on multi-modal MR images. The MMTSN is composed of
three sub-branches and a main branch. Specifically, the sub-branches are used
to capture different tumor features from multi-modal images, while in the main
branch, we design a spatial-channel fusion block (SCFB) to effectively
aggregate multi-modal features. Additionally, inspired by the fact that the
spatial relationship between sub-regions of tumor is relatively fixed, e.g.,
the enhancing tumor is always in the tumor core, we propose a spatial loss to
constrain the relationship between different sub-regions of tumor. We evaluate
our method on the test set of multi-modal brain tumor segmentation challenge
2020 (BraTs2020). The method achieves 0.8764, 0.8243 and 0.773 dice score for
whole tumor, tumor core and enhancing tumor, respectively.
