Few-shot segmentation and fiber tractography of human vagus nerve using 3D-MUSE imaging.

We are dissecting and imaging 100 human cadaver nerves with unprecedented range of anatomical coverage (from brainstem to abdomen) and imaging modalities. Our teams used 3D serial block-face Microscopy with Ultraviolet Surface Excitation (3D-MUSE) to image, visualize, and quantify the morphology and microanatomy of the human vagus nerve, providing three-dimensional insights into its structure and functional organization. We prepared 3-mm and 5-mm-long samples of human cervical vagus and median nerve using various staining and embedding techniques before imaging with 0.9-μm in-plane resolution and between 3-μm and 12-μm slice thickness. Staining quality varied across samples thus requiring training of a sample-based neural network rather than using a generalized analysis algorithm. We used few-shot learning to segment the fascicles, perineurium, and epineurium regions. We trained a 2D U-Net architecture with 4-8% of each sample's images before applying to a held-out test set. Performance achieved a mean Dice score range of 0.85±0.10 and 0.93±0.05 across various 3D-MUSE samples. We also investigated an initial pre-training step of the U-Net model to improve segmentation performance. Pre-training enabled the segmentation model to have better awareness of splitting fascicles in the held-out test set. From sample segmentation predictions, morphologic metrics such as nerve diameter, fascicle count, fascicle area, fascicle diameter, and perineurium thickness are calculated. Nerve fiber tractography from sample images highlight dynamic fascicle organization throughout 3-mm nerve samples These results demonstrate the importance and success of sample-based training for segmentation and nerve fiber tractography, with further training anticipated to yield even better outcomes.