We propose a reliable and energy-efficient framework for 3D brain tumor segmentation using spiking neural networks (SNNs). A multi-view ensemble of sagittal, coronal, and axial SNN models provides voxel-wise uncertainty estimation and enhances segmentation robustness. To address the high computational cost in training SNN models for semantic image segmentation, we employ Forward Propagation Through Time (FPTT), which maintains temporal learning efficiency with significantly reduced computational cost. Experiments on the Multimodal Brain Tumor Segmentation Challenges (BraTS 2017 and BraTS 2023) demonstrate competitive accuracy, well-calibrated uncertainty, and an 87% reduction in FLOPs, underscoring the potential of SNNs for reliable, low-power medical IoT and Point-of-Care systems.

翻译：我们提出了一种利用脉冲神经网络（SNNs）进行三维脑肿瘤分割的可靠且高能效框架。通过集成矢状面、冠状面和轴状面视图的SNN模型进行多视角集成，提供了体素级的不确定性估计，并增强了分割的鲁棒性。为了解决训练用于语义图像分割的SNN模型时计算成本高的问题，我们采用了时间前向传播（FPTT）方法，该方法在显著降低计算成本的同时保持了时间学习效率。在多模态脑肿瘤分割挑战赛（BraTS 2017和BraTS 2023）数据集上的实验表明，该方法具有竞争力的准确性、良好校准的不确定性，并将浮点运算次数（FLOPs）减少了87%，凸显了SNN在可靠、低功耗的医疗物联网和床旁系统中的应用潜力。