Open-source, deep-learning skin surface segmentation model for cost-effective neuronavigation accessible to low-resource settings

Abstract

Purpose: Neuronavigation has become integral to neurosurgery. By helping neurosurgeons localize their surgical instruments according to the patient's anatomy in real time, neuronavigation facilitates improved patient outcomes and lower procedure costs. This technology has been out of reach of surgeons in lower resource settings due to cost and infrastructure limitations. Our group has developed NousNav, an open-source low-cost navigation system accessible in such environments to address this need. Neuronavigation requires skin surface segmentation from preoperative images, which is necessary for image-to-patient registration. Thus, accurate skin segmentation is essential for effective neuronavigation. However, manual skin surface segmentation is time-consuming, reliant on technical expertise, and susceptible to inter-rater variability. We propose to implement deep learning into the skin surface segmentation step of NousNav, mitigating the limitations of manual segmentation. Approach:We built a nnU-Net model to automate skin segmentation, training it on 36 healthy brain T1-FLASH MRIs and testing it on another 5 healthy brain MRIs. We evaluated the model's performance versus that of three human raters, using Hausdorff distance (in mm) and Dice metrics.Results: On average, comparing the human raters to each other and the automated segmentation model to the human raters yielded Hausdorff distances of 1.3534mm and 1.0198mm and Dice scores of 0.9750 and 0.9820, respectively, demonstrating that automated skin surface segmentation performs comparably to manual segmentation.Conclusions: Here we uniquely implemented automated segmentation for meeting the unmet need of accessible, open-source neuronavigation systems. Considering the promising performance of our deep learning model, this step may be feasibly incorporated into the NousNav workflow.