4 researchers · 4 publications
Develop accurate in-silico models of human anatomy using multimodal imaging and AI. This vertical is creating the most detailed human vagus nerve digital twin in existence, processing over 4 million microscopy and micro-CT images (approximately 200 terabytes of data) from 58 vagus nerves (left and right from 29 cadavers) to map the spatial arrangement of fascicles and fibers from the cervical and thoracic trunks to organ-level branches. Funded by a $6.7M NIH SPARC REVA grant, this work leverages deep learning architectures—including nnU-Net for 3D micro-CT segmentation and Mask2Former for individual identification of myelinated and unmyelinated axonal cross sections in immunohistochemistry data—to quantify vagus nerve anatomy and inform the design of next-generation selective vagus nerve stimulation devices.