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.
@article{arxiv.2402.05218,
title = {Self-calibrated convolution towards glioma segmentation},
author = {Felipe C. R. Salvagnini and Gerson O. Barbosa and Alexandre X. Falcao and Cid A. N. Santos},
journal= {arXiv preprint arXiv:2402.05218},
year = {2024}
}