Self-calibrated convolution towards glioma segmentation
Felipe C. R. Salvagnini Gerson O. Barbosa Alexandre X. Falcao Cid A. N. Santos
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.