Segmentation of clinical imagery for improved epidural stimulation to address spinal cord injury

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Abstract

Spinal cord injury (SCI) can severely impair motor and autonomic function, with long-term consequences for quality of life. Epidural stimulation has emerged as a promising intervention, offering partial recovery by activating neural circuits below the injury. To make this therapy effective in practice, precise placement of stimulation electrodes is essential -- and that requires accurate segmentation of spinal cord structures in MRI data. We present a protocol for manual segmentation tailored to SCI anatomy, and evaluated a deep learning approach using a U-Net architecture to automate this segmentation process. Our approach yields accurate, efficient segmentation that identify potential electrode placement sites with high fidelity. Preliminary results suggest that this framework can accelerate SCI MRI analysis and improve planning for epidural stimulation, helping bridge the gap between advanced neurotechnologies and real-world clinical application with faster surgeries and more accurate electrode placement.

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