ScholarWorks@성균관대학교

초록

Precise brain tumor segmentation is essential for reliable diagnosis, treatment planning, and clinical follow-up. Despite recent progress in deep learning, most existing models remain predominantly data-driven and lack mechanisms to incorporate fundamental domain knowledge, including the physics of MRI acquisition, tumor morphology, and the biological dynamics of tumor progression. To bridge this gap, we propose HMP-Net, a theory-guided hierarchical multi-prior network that explicitly embeds these principles into the feature learning process. HMP-Net integrates three complementary levels of prior knowledge: (1) a shallow physical signal encoder that models inter-modal coupling in multimodal MRI data, (2) a mid-level topological analyzer that extracts Betti number–based structural priors through differentiable approximations, and (3) a deep tumor dynamics modeler that solves reaction–diffusion equations to capture biologically plausible tumor growth patterns. Extensive experiments on the BraTS 2021 and BraTS 2018 benchmarks demonstrate that HMP-Net surpasses state-of-the-art approaches, achieving average Dice scores of 91.55% and 86.58%, respectively. Ablation studies further validate the contribution of each hierarchical prior and show that the learned parameters maintain clear physical interpretability. These results demonstrate that embedding multi-scale, domain-specific priors into deep architectures substantially enhances generalization, interpretability, and clinical relevance, offering a new paradigm for knowledge-driven medical image analysis. The code will be available at https://github.com/kanglzu/hmp_net.

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