  Convolutional neural networks (CNNs) have been successfully used for brain
tumor segmentation, specifically, fully convolutional networks (FCNs). FCNs can
segment a set of voxels at once, having a direct spatial correspondence between
units in feature maps (FMs) at a given location and the corresponding
classified voxels. In convolutional layers, FMs are merged to create new FMs,
so, channel combination is crucial. However, not all FMs have the same
relevance for a given class. Recently, in classification problems,
Squeeze-and-Excitation (SE) blocks have been proposed to re-calibrate FMs as a
whole, and suppress the less informative ones. However, this is not optimal in
FCN due to the spatial correspondence between units and voxels. In this
article, we propose feature recombination through linear expansion and
compression to create more complex features for semantic segmentation.
Additionally, we propose a segmentation SE (SegSE) block for feature
recalibration that collects contextual information, while maintaining the
spatial meaning. Finally, we evaluate the proposed methods in brain tumor
segmentation, using publicly available data.
