  Early-stage 3D brain tumor segmentation from magnetic resonance imaging (MRI)
scans is crucial for prompt and effective treatment. However, this process
faces the challenge of precise delineation due to the tumors' complex
heterogeneity. Moreover, energy sustainability targets and resource
limitations, especially in developing countries, require efficient and
accessible medical imaging solutions. The proposed architecture, a Lightweight
3D ATtention U-Net with Parallel convolutions, LATUP-Net, addresses these
issues. It is specifically designed to reduce computational requirements
significantly while maintaining high segmentation performance. By incorporating
parallel convolutions, it enhances feature representation by capturing
multi-scale information. It further integrates an attention mechanism to refine
segmentation through selective feature recalibration. LATUP-Net achieves
promising segmentation performance: the average Dice scores for the whole
tumor, tumor core, and enhancing tumor on the BraTS2020 dataset are 88.41%,
83.82%, and 73.67%, and on the BraTS2021 dataset, they are 90.29%, 89.54%, and
83.92%, respectively. Hausdorff distance metrics further indicate its improved
ability to delineate tumor boundaries. With its significantly reduced
computational demand using only 3.07 M parameters, about 59 times fewer than
other state-of-the-art models, and running on a single V100 GPU, LATUP-Net
stands out as a promising solution for real-world clinical applications,
particularly in settings with limited resources. Investigations into the
model's interpretability, utilizing gradient-weighted class activation mapping
and confusion matrices, reveal that while attention mechanisms enhance the
segmentation of small regions, their impact is nuanced. Achieving the most
accurate tumor delineation requires carefully balancing local and global
features.
