Self-calibrated convolution towards glioma segmentation

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 segme...

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Bibliographic Details
Published in2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM) pp. 1 - 4
Main Authors Salvagnini, Felipe C. R., Barbosa, Gerson O., Falcao, Alexandre X., Santos, Cid A. N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.11.2023
Subjects
3D Image Segmentation
Artificial neural networks
Brain
Image segmentation
Medical Image Analysis
Network architecture
Neural Networks
Protocols
Three-dimensional displays
Visualization
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Summary: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.
DOI:10.1109/SIPAIM56729.2023.10373517