A multiplicative additive bias variational framework for accurate and interpretable brain MRI segmentation in cloud-based medical imaging systems

Abstract

Accurate brain magnetic resonance imaging (MRI) segmentation remains challenging due to intensity inhomogeneity, acquisition-related bias fields, and ambiguous tissue boundaries. To address these challenges, a Multiplicative–Additive Bias Single-Function Dual-Level-Set (MAB-SFDLS) model is introduced within a Software-as-a-Service (SaaS)-based medical image analysis framework. The model incorporates both multiplicative and additive bias components into a unified variational energy formulation and employs a single level-set function with dual thresholds to achieve stable multi-region segmentation with smooth and continuous boundaries. The method was evaluated on the MRBrainS18 dataset, achieving Dice scores of 0.95 for white matter and 0.86 for gray matter, with a boundary deviation of 2.20 mm measured using HD95. Compared with the classical level-set formulation, notable improvements were observed in both overlap accuracy and boundary precision. The approach also demonstrated competitive performance against state-of-the-art deep learning models, including nnU-Net and U-Mamba, while maintaining lower computational requirements. Statistical analysis confirmed that the improvements were significant (p < 0.05). To enhance interpretability and practical applicability, the segmentation framework is integrated with a browser-based 3D visualization module that supports synchronized surface and volume rendering, as well as interactive region-of-interest exploration. This framework provides a practical, interpretable, computationally efficient, and scalable approach to robust brain MRI segmentation in a cloud-based medical imaging environment. The proposed model code and SaaS platform prototype are publicly available at https://doi.org/10.5281/zenodo.20797546.