COUNTEN, an AI-Driven Tool for Rapid and Objective Structural Analyses of the Enteric Nervous System

Autor: Narayani Wagle, Monalee Saha, Julieta Gomez-Frittelli, Cedric Espenel, Archana Venkataraman, Yuta Kobayashi, Julia A. Kaltschmidt, Subhamoy Das, Shriya Bakshi, Subhash Kulkarni, Alicia Bukowski
Rok vydání: 2021
Předmět:
Zdroj: eNeuro
ISSN: 2373-2822
Popis: The enteric nervous system (ENS) consists of an interconnected meshwork of neurons and glia residing within the wall of the gastrointestinal (GI) tract. While healthy GI function is associated with healthy ENS structure, defined by the normal distribution of neurons within ganglia of the ENS, a comprehensive understanding of normal neuronal distribution and ganglionic organization in the ENS is lacking. Current methodologies for manual enumeration of neurons parse only limited tissue regions and are prone to error, subjective bias, and peer-to-peer discordance. There is accordingly a need for robust, and objective tools that can capture and quantify enteric neurons within multiple ganglia over large areas of tissue. Here, we report on the development of an AI-driven tool, COUNTEN (COUNTing Enteric Neurons), which is capable of accurately identifying and enumerating immunolabeled enteric neurons, and objectively clustering them into ganglia. We tested and found that COUNTEN matches trained humans in its accuracy while taking a fraction of the time to complete the analyses. Finally, we use COUNTEN’s accuracy and speed to identify and cluster thousands of ileal myenteric neurons into hundreds of ganglia to compute metrics that help define the normal structure of the ileal myenteric plexus. To facilitate reproducible, robust, and objective measures of ENS structure across mouse models, experiments, and institutions, COUNTEN is freely and openly available to all researchers.
Databáze: OpenAIRE