Proceedings Article | 2 April 2024
KEYWORDS: Image segmentation, Education and training, Nerve, Deep learning, 3D modeling, Anatomy, Visual process modeling, Transformers, Network architectures, Data modeling
We are microscopically imaging and analyzing the human vagus nerve (VN) anatomy to create the first ever VN connectome to support modeling of neuromodulation therapies. Although micro-CT and MRI roughly identify vagus nerve anatomy, they lack the spatial resolution required to identify small fascicle splitting and merging, and perineurium boundaries. We developed 3D serial block-face Microscopy with Ultraviolet Surface Excitation (3D-MUSE), with 0.9- µm in-plane resolution and 3-μm cut thickness. 3D-MUSE is ideal for VN imaging, capturing large, myelinated fibers, connective sheaths, fascicle dynamics, and nerve bundle tractography. Each 3-mm 3D-MUSE ROI generates approximately 1,000 grayscale images, necessitating automatic segmentation as over 50-hrs were spent manually annotating fascicles, perineurium, and epineurium in every 20th image, giving 50 images. We trained three types of multi-class deep learning segmentation models. First, 25 annotated images trained a 2D U-Net and Attention U-Net. Second, we trained a Vision Transformer (ViT) using self-supervised learning with 200 unlabeled images before refining the ViT’s initialized weights of a U-Net Transformer with 25 training images and labels. Third, we created pseudo-3D images by concatenating each annotated image with an image ±k slices apart (k=1,10), and trained a 2D U-Net similarly. All models were tested on 25 held-out images and evaluated using Dice. While all trained models performed comparably, the 2D U-Net model trained on pseudo-3D images demonstrated highest Dice values (0.936). With sample-based-training, one obtains very promising results on thousands of images in terms of segmentation and nerve fiber tractography estimation. Additional training from more samples could obtain excellent results.
