This protocol describes a step-by-step computational workflow for the density measurement of cholinergic innervation in three-dimensional (3D) images of the pig colonic enteric nervous system (ENS) with Imaris 9.7 for neuroscientist. This workflow will also work on all other gastrointestinal segments from different species. The quantitative analysis of cholinergic innervation in the ENS of gastrointestinal tract is challenging because the nerve fibers are distributed within a 3D space rather than lining on the same plane in each enteric plexus. The traditional way of counting nerve fibers manually via grid-based stereology and histomorphometry is both time consuming and labor intensive (Hunter et al. 2020). We sought to develop a reliable and computerized method to quantify the density of cholinergic innervation in 3D images of the pig colonic ENS generated from z-stack confocal images with whole mount preparation of pig colonic enteric plexuses. Central and peripheral cholinergic innervation was labeled by double-immunolabeling with a novel mouse anti-human peripheral type of choline acetyltransferase (hpChAT) antibody combined with a rabbit anti-the common type of ChAT (cChAT) antibody, a reliable marker of cholinergic neurons in the central nervous system (Bellier et al., 2019). Imaris 9.7 Surfaces Rendering Technology (https://imaris.oxinst.com/products/imaris-for-neuroscientists) was adapted the to automatically create surfaces on cChAT immunoreactive (ir) nerve fibers, hpChAT-ir fibers + somata, and ganglia containing neurons and fibers. The volumes of surface-masked structures were automatically measured using the software Imaris 9.7. The densities were calculated and expressed as percentages of their volumes in ganglion volumes (v/v, %). This approach allows us to directly, objectively and automatically measure the densities of neurons and fibers with less biases due to observer/examiner judgment. It is also faster than counting manually and allows us to quantitate a larger number of samples, increasing statistical accuracy.