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Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Training

arXiv 2601.16652 ↗First posted 23 Jan 2026cs.CV cs.NEcited by 0

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Abstract (arXiv)

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.

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References · 13

  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  2. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  3. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  4. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  5. 2020Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applicationscited by 34
  6. 2021Accurate online training of dynamical spiking neural networks through Forward Propagation Through Timecited by 33
  7. 2021A Spiking Neural Network for Image Segmentationcited by 27
  8. 2022HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalitiescited by 24
  9. 2023The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)cited by 24
  10. 2023Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentationcited by 5
  11. 2023Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropoutcited by 5
  12. 2025Efficient Event-Based Semantic Segmentation via Exploiting Frame-Event Fusion: A Hybrid Neural Network Approachcited by 5
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  1. 2024Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regressioncited by 1

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