Three-dimensional reconstruction of internal fascicles and microvascular structures of human peripheral nerves

Biofabricated nanostructured and microstructured scaffolds have exhibited great potential for nerve tissue regeneration and functional restoration, and prevascularization and biotransportation within 3D fascicle structures are critical. Unfortunately, an ideal internal fascicle and microvascular model of human peripheral nerves is lacking. In this study, we used microcomputed tomography (microCT) to acquire high‐resolution images of the human sciatic nerve. We propose a novel deep‐learning network technique, called ResNetH3D‐Unet, to segment fascicles and microvascular structures. We reconstructed 3D intraneural fascicles and microvascular topography to quantify the fascicle volume ratio (FVR), microvascular volume ratio (MVR), microvascular to fascicle volume ratio (MFVR), fascicle surface area to volume ratio (FSAVR), and microvascular surface area to volume ratio (MSAVR) of human samples. The frequency distributions of the fascicle diameter, microvascular diameter, and fascicle‐to‐microvasculature distance were analyzed. The obtained microCT analysis and reconstruction provided high‐resolution microstructures of human peripheral nerves. Our proposed ResNetH3D‐Unet method for fascicle and microvasculature segmentation yielded a mean intersection over union (IOU) of 92.1% (approximately 5% higher than the U‐net IOU). The 3D reconstructed model showed that the internal microvasculature runs longitudinally within the internal epineurium and connects to the external vasculature at some points. Analysis of the 3D data indicated a 48.2 ± 3% FVR, 23.7 ± 1.8% MVR, 4.9 ± 0.5% MFVR, 7.26 ± 2.58 mm‐1 FSAVR, and 1.52 ± 0.52 mm‐1 MSAVR. A fascicle diameter of 0.98 mm, microvascular diameter of 0.125 mm, and microvasculature‐to‐fascicle distance of 0.196 mm were most frequent. This study provides fundamental data and structural references for designing bionic scaffolding constructs with 3D microvascular and fascicle distributions.