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A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation.

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Accurately locating burn wounds is crucial for patient treatment. This study introduces a lightweight spiking neural network for precise burn wound segmentation, achieving high accuracy on edge devices.

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burn imageimage segmentationretinal ganglion cellsspiking neural network

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Area of Science:

  • Medical Imaging
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Severe burn injuries cause increased catabolism and significant fluid loss, leading to high mortality rates.
  • Accurate estimation of burn wound area (as a percentage of Total Body Surface Area - TBSA%) is critical for early patient treatment and fluid management.
  • Current methods for burn wound assessment suffer from observer variability, necessitating objective and accurate localization techniques.

Purpose of the Study:

  • To develop an objective and accurate method for burn wound segmentation.
  • To address the limitations of existing Convolutional Neural Networks (CNNs) regarding computational resources on edge hardware.
  • To propose a lightweight model for efficient burn wound segmentation.

Main Methods:

  • Construction of a dedicated burn image dataset.
  • Development of a U-type Spiking Neural Network (SNN) model inspired by retinal ganglion cells (RGC) for segmenting burn and non-burn areas.
  • Introduction of a cross-layer skip concatenation module to enhance network performance.

Main Results:

  • The proposed U-type SNN model achieved a pixel accuracy of 92.89% for burn wound segmentation.
  • The network requires only 16.6 Mbytes of parameters, demonstrating its lightweight nature.
  • The model exhibited remarkable accuracy and suitability for edge hardware.

Conclusions:

  • The developed lightweight SNN model offers an accurate and efficient solution for burn wound segmentation.
  • This approach overcomes the computational constraints of traditional CNNs for edge deployment.
  • The findings highlight the potential of SNNs in medical imaging for critical care applications.