  Accurate brain tumor segmentation in the early stages of the disease is
crucial for the treatment's effectiveness, avoiding exhaustive visual
inspection of a qualified specialist on 3D MR brain images of multiple
protocols (e.g., T1, T2, T2-FLAIR, T1-Gd). Several networks exist for Glioma
segmentation, being nnU-Net one of the best. In this work, we evaluate
self-calibrated convolutions in different parts of the nnU-Net network to
demonstrate that self-calibrated modules in skip connections can significantly
improve the enhanced-tumor and tumor-core segmentation accuracy while
preserving the wholetumor segmentation accuracy.
