Supervised Machine Learning Segmentation and Quantification of Gastric Pacemaker Cells.

Interstitial Cells of Cajal (ICC) are specialized pacemaker cells that generate and actively propagate electrophysiological events called slow waves. Slow waves regulate the motility of the gastrointestinal tract necessary for digesting food. Degradation in the ICC network structure has been qualitatively associated to several gastrointestinal motility disorders. ICC network structure can be obtained using confocal microscopy, but the current limitations in imaging and segmentation techniques have hindered an accurate representation of the networks. In this study, supervised machine learning techniques were applied to extract the ICC networks from 3D confocal microscopy images. The results showed that the Fast Random Forest classification method using Trainable WEKA Segmentation outperformed the Decision Table and Naïve Bayes classification methods in sensitivity, accuracy, and F-measure. Using the Fast Random Forest classifier, 12 gastric antrum tissue blocks were segmented and variations in ICC network thickness, density and process width were quantified for the myenteric plexus ICC network (the primary pacemakers). Our findings demonstrated regional variation in ICC network density and thickness along the circumferential and longitudinal axis of the mouse antrum. An inverse relationship was observed in the distal and proximal antrum for density (proximal: 9.8±4.0% vs distal: 7.6±4.6%) and thickness (proximal: 15±3 μm vs distal: 24±10 μm). Limited variation in ICC process width was observed throughout the antrum (5±1 μm).Clinical Relevance- Detailed quantification of regional ICC structural properties will provide insights into the relationship between ICC structure, slow waves and resultant gut motility. This will improve techniques for the diagnosis and treatment of functional GI motility disorders.