2 research outputs found
Generative modeling of the enteric nervous system employing point pattern analysis and graph construction
We describe a generative network model of the architecture of the enteric
nervous system (ENS) in the colon employing data from images of human and mouse
tissue samples obtained through confocal microscopy. Our models combine spatial
point pattern analysis with graph generation to characterize the spatial and
topological properties of the ganglia (clusters of neurons and glial cells),
the inter-ganglionic connections, and the neuronal organization within the
ganglia. We employ a hybrid hardcore-Strauss process for spatial patterns and a
planar random graph generation for constructing the spatially embedded network.
We show that our generative model may be helpful in both basic and
translational studies, and it is sufficiently expressive to model the ENS
architecture of individuals who vary in age and health status. Increased
understanding of the ENS connectome will enable the use of neuromodulation
strategies in treatment and clarify anatomic diagnostic criteria for people
with bowel motility disorders.Comment: 17 pages, 5 figure
High-throughput segmentation of unmyelinated axons by deep learning
Axonal characterizations of connectomes in healthy and disease phenotypes are surprisingly incomplete and biased because unmyelinated axons, the most prevalent type of fibers in the nervous system, have largely been ignored as their quantitative assessment quickly becomes unmanageable as the number of axons increases. Herein, we introduce the first prototype of a high-throughput processing pipeline for automated segmentation of unmyelinated fibers. Our team has used transmission electron microscopy images of vagus and pelvic nerves in rats. All unmyelinated axons in these images are individually annotated and used as labeled data to train and validate a deep instance segmentation network. We investigate the effect of different training strategies on the overall segmentation accuracy of the network. We extensively validate the segmentation algorithm as a stand-alone segmentation tool as well as in an expert-in-the-loop hybrid segmentation setting with preliminary, albeit remarkably encouraging results. Our algorithm achieves an instance-level F1 score of between 0.7 and 0.9 on various test images in the stand-alone mode and reduces expert annotation labor by 80% in the hybrid setting. We hope that this new high-throughput segmentation pipeline will enable quick and accurate characterization of unmyelinated fibers at scale and become instrumental in significantly advancing our understanding of connectomes in both the peripheral and the central nervous systems