Accurate and effective segmentation of ischemic stroke lesions in Magnetic Resonance Imaging is essential for timely clinical intervention and treatment planning. Although Convolutional Neural Networks (CNNs) perform remarkably well in extracting spatial features, they may benefit from complementary temporal denoising mechanisms when handling the intrinsic noise present in stroke imaging. This paper presents a hybrid CNN–SNN framework that combines the spatial learning capability of CNNs with th