Building hybrid models of neuromodulation from automatic segmentation of peripheral nerve histological sections

Electrical stimulation of peripheral nerves offers a way to restore sensory-motor functions and treat drug-resistant conditions affecting internal organs. Understanding the fascicular organization of the implanted nerves is essential for enhancing the selective neuromodulation of the targeted bodily functions. In fact, this knowledge can inform the development of computational models that can be used to optimize electrode design and stimulation protocols. Traditionally, peripheral nerve topographies are segmented manually to highlight fascicle contours, resulting in a labor-intensive and error-prone process. In this study, we present a UNet-based deep neural network for automatic segmentation of fascicles from nerve histological sections, trained on original data from different nerves and stained with different techniques. The model leverages a pretrained encoder, reducing the need for extensive training datasets and allowing us to generalize to nerve types and histological stains previously unseen during training. The quality of the resulting segmentation has been evaluated using both the Dice coefficient and domain-specific metrics tailored to assess the quality of the reconstructed fascicle topography. Furthermore, we employed automatically segmented nerve sections to build computational models of peripheral nerve stimulation and assess the impact of segmentation on the accuracy of fascicle-wise recruitment predictions. Our results highlight that automated segmentation can reliably inform the modeling of neuromodulation applications, with minimal error in predicting recruitment thresholds. This approach paves the way for harnessing the large quantities of histological data that can be extracted from cadaveric nerve samples for use in computational models of neural interfaces, potentially advancing the design of next generation neuroprosthetic and bioelectronic medicine applications.