Self-calibrated convolution towards glioma segmentation
This work addresses the need for more accurate automated segmentation to assist specialists in early-stage brain tumor treatment, though it is incremental as it builds on the established nnU-Net framework.
The paper tackled the problem of improving glioma segmentation accuracy in 3D MR brain images by integrating self-calibrated convolutions into the nnU-Net network, resulting in significant improvements in enhanced-tumor and tumor-core segmentation while maintaining wholetumor accuracy.
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.