Enhancing 3D MRI-Based Necrotic Core Segmentation in Glioblastoma Using Activation Functions in Deep Learning

Abstract

Precise brain tumour delineation is vital for therapy protocols and tracking. However, standard Rectified Linear Units (ReLU) struggle to capture subtle necrotic-core variations due to zero-gradient behaviour in the negative domain. To address this, we present a controlled benchmark of 12 activation functions within a fixed Residual 3D U-Net using the Brain Tumour Segmentation (BraTS) 2020 dataset. In the single-run benchmark, Swish achieved the best necrotic-core (NCR) Dice (0.676; +2.0% over ReLU, p < 0.01), while TanhExp attained the highest whole-tumour accuracy (0.879). To test the reliability of these single-run results, the four functions central to our claims were retrained across three random seeds. This analysis confirmed a small but consistent NCR advantage for the smooth and adaptive functions—Swish (0.677 ± 0.003) and PReLU (0.678 ± 0.004) over ReLU (0.661 ± 0.012; pooled p < 0.001)—with Swish among the most stable functions in this region. By contrast, the apparent single-run differences in the enhancing tumour, and the underperformance of PReLU, did not generalise across seeds, indicating that activation-function effects in this task are concentrated in the necrotic core and that single-seed comparisons can be misleading. Crucially, Swish achieved these gains with zero additional trainable parameters and only a ~1% training latency penalty on common hardware. Replacing ReLU with Swish offers a cost-effective, architecture-preserving strategy to improve segmentation reliability and boundary delineation. Ultimately, this zero-cost architectural modification is a promising, preliminary step towards more reliable automated tumour delineation, pending prospective validation on multi-institutional data and expert radiological assessment.