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arXiv · 2402.05218

Self-calibrated convolution towards glioma segmentation

Felipe C. R. Salvagnini·Gerson O. Barbosa·Alexandre X. Falcao·Cid A. N. Santos
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Abstract

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.

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Felipe C. R. Salvagnini, Gerson O. Barbosa, Alexandre X. Falcao, Cid A. N. Santos. 2024-02-07. Self-calibrated convolution towards glioma segmentation. https://doi.org/10.1109/sipaim56729.2023.10373517

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