Self-calibrated convolution towards glioma segmentation

• URL: http://arxiv.org/abs/2402.05218v1
• Date: Wed, 7 Feb 2024 19:51:13 GMT
• Title: Self-calibrated convolution towards glioma segmentation
• Authors: Felipe C. R. Salvagnini and Gerson O. Barbosa and Alexandre X. Falcao and Cid A. N. Santos
• Abstract summary: 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.
• Score: 45.74830585715129
• License: http://creativecommons.org/licenses/by/4.0/
• 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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