Enhanced brain tumor segmentation in medical imaging using multi-modal multi-scale contextual aggregation and attention fusion
Waqar Aslam, Jawad Hussain, Muhammad Zeeshan Aslam, Salman Jan, Talha Bin Riaz, Adeel Iqbal, Mohammad Arif, I.B. Khan
Abstract
Accurate segmentation of brain tumors from multi-modal MRI scans is critical for diagnosis, treatment planning, and disease monitoring. Tumor heterogeneity and inter-image variability across MRI sequences pose challenging problems to state-of-the-art segmentation models. This paper presents a novel Multi-Modal Multi-Scale Contextual Aggregation with Attention Fusion (MM-MSCA-AF) framework that leverages multi-modal MRI images (T1, T2, FLAIR, and T1-CE) to enhance segmentation performance. The model employs multi-scale contextual aggregation to obtain global and fine-grained spatial features, and gated attention fusion for selectively refining effective feature representations and discarding noise. Evaluated on the BRATS 2020 dataset, MM-MSCA-AF achieves a Dice value of 0.8158 for necrotic tumor regions and 0.8589 in total, outperforming state-of-the-art architectures such as U-Net, nnU-Net, and Attention U-Net. These results demonstrate the effectiveness of MM-MSCA-AF in handling complex tumor shapes and improving segmentation accuracy. The proposed approach has significant clinical value, offering a more accurate and automatic brain tumor segmentation solution in medical imaging.