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<p>Medical image segmentation is crucial for computer-aided diagnosis, but segmenting complex structures like brain extracellular space (ECS) remains challenging due to low contrast and noise. Accurate ECS segmentation is vital for identifying biomarkers in neurological diseases such as Alzheimer’s. This study proposes ECSNN-SEG, a two-stage spiking neural network based on the biologically plausible ECS-LIF neuron model. It integrates a MUNet variant for coarse segmentation and an ECS-LIF-based refinement network to enhance accuracy. Evaluations on the cryo-EM ECSSeg dataset show ECSNN-SEG (BPTT) achieves superior performance (ACC: 0.9833 ± 0.0017, F1-score: 0.8647 ± 0.0079) compared with state-of-the-art models, as well as high robustness under noise.</p>

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Integrating Neuroscientific Priors into Spiking Neural Networks: ECSNN-SEG for Robust Brain ECS Segmentation from Low-SNR Cryo-electron Microscopy Data

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Chao Zhang,
  • Mingdong Wang,
  • Shufan Yang,
  • Desheng Zhao,
  • Ce Gao,
  • Feng Yin,
  • Ping Ren,
  • Yusong Ge,
  • Xiaohong Wang

摘要

Medical image segmentation is crucial for computer-aided diagnosis, but segmenting complex structures like brain extracellular space (ECS) remains challenging due to low contrast and noise. Accurate ECS segmentation is vital for identifying biomarkers in neurological diseases such as Alzheimer’s. This study proposes ECSNN-SEG, a two-stage spiking neural network based on the biologically plausible ECS-LIF neuron model. It integrates a MUNet variant for coarse segmentation and an ECS-LIF-based refinement network to enhance accuracy. Evaluations on the cryo-EM ECSSeg dataset show ECSNN-SEG (BPTT) achieves superior performance (ACC: 0.9833 ± 0.0017, F1-score: 0.8647 ± 0.0079) compared with state-of-the-art models, as well as high robustness under noise.