Rapid advancements in high-definition CMOS and magnetic resonance transducers have led to the accumulation of complex medical imaging data that requires robust, real-time computational interpretation. However, current high-performance segmentation models require excessive computational power, making them incompatible with low-power point-of-care sensing hardware. Therefore, we improved the Self-Supervised Dynamic Gated Fusion Network (SS-DGFNet) model for resource-efficient medical image segmentation. The network utilizes automated signal calibration (self-supervised learning) and an adaptive fusion module to maintain high accuracy even with missing sensor data or limited labeled information. For the Multimodal Brain Tumor Image Segmentation Benchmark 2025 dataset, SS-DGFNet shows high spatial accuracy (a Dice score of 0.888) while maintaining 97.6% performance retention when a sensor channel is lost. Despite these gains, issues remain, including the need for validation across a broader range of clinical sensor materials and the optimization of the model for heterogeneous edge-computing hardware. The improved model demonstrates significant robustness when a sensor modality is missing. By reducing computational overhead and accelerating calibration cycles for emerging biosensors, the model leads to the transition of complex diagnostics to edge-computing sensor platforms and supports the transition of complex diagnostics to mobile sensor platforms.