Three-Dimensional Fractal Analysis of the Interstitial Cells of Cajal Networks of Gastrointestinal Tissue Specimens.

Introduction: Several functional gastrointestinal disorders (FGIDs) have been associated with the degradation or remodeling of the network of interstitial cells of Cajal (ICC). Introducing fractal analysis to the field of gastroenterology as a promising data analytics approach to extract key structural characteristics that may provide insightful features for machine learning applications in disease diagnostics. Fractal geometry has advantages over several physically based parameters (or classical metrics) for analysis of intricate and complex microstructures that could be applied to ICC networks. Methods: In this study, three fractal structural parameters: Fractal Dimension, Lacunarity, and Succolarity were employed to characterize scale-invariant complexity, heterogeneity, and anisotropy; respectively of three types of gastric ICC network structures from a flat-mount transgenic mouse stomach. Results: The Fractal Dimension of ICC in the longitudinal muscle layer was found to be significantly lower than ICC in the myenteric plexus and circumferential muscle in the proximal, and distal antrum, respectively (both p < 0.0001). Conversely, the Lacunarity parameters for ICC-LM and ICC-CM were found to be significantly higher than ICC-MP in the proximal and in the distal antrum, respectively (both p < 0.0001). The Succolarity measures of ICC-LM network in the aboral direction were found to be consistently higher in the proximal than in the distal antrum (p < 0.05). Conclusions: The fractal parameters presented here could go beyond the limitation of classical metrics to provide better understanding of the structural-functional relationship between ICC networks and the conduction of gastric bioelectrical slow waves.

Deciphering Stomach Myoelectrical Slow Wave Conduction Patterns via Confocal Imaging of Gastric Pacemaker Cells and Fractal Geometry.

Interstitial Cells of Cajal (ICC) are specialized gastrointestinal (GI) pacemaker cells that generate and actively propagate slow waves of depolarization (SWs) of the muscularis propria. SWs regulate the motility of the GI tract necessary for digestion, absorption of nutrients, and elimination of waste. Within the gastric wall, there are three main inter-connected layers of ICC networks: longitudinal muscle ICC (ICC-LM), myenteric plexus ICC (ICC-MP) & circumferential muscle (ICC-CM). Fractal structural parameters such as Fractal Dimension (FD), Lacunarity and Succolarity, have many advantages over physically-based parameters when it comes to characterizing the complex architectures of ICC networks. The analysis of networks of ICC throughout the proximal and distal murine gastric antrum with the FD and Lacunarity metrics was previously performed. Although the application of Succolarity is relatively nascent compared to the FD and Lacunarity; nevertheless, numerous studies have demonstrated the capability of this fractal measure to extract information from images associated with flow by which neither the FD nor Lacunarity are capable of discerning. In this study, Succolarity analysis of ICC-MP and ICC-CM networks were performed with confocal images taken across the proximal and distal murine antrum. Our findings demonstrated the Succolarity of ICC-MP and ICC-CM varied with directions and antral regions. The Succolarity of ICC-MP did not vary considerably with direction, however, Succolarity was higher in the aboral direction with 0.2113 ±0.1589, and 0.0637 ±0.0822 in the proximal and distal antrum, respectively. The overall Succolarity of ICC-MP was significantly higher than that of ICC-CM in the proximal antrum ( 0.1580±0.1325 vs [Formula: see text]) and in the distal antrum ( 0.0449 ±0.0409 vs [Formula: see text]). Clinical Relevance-Modeling SWs conduction patterns via image analysis of detailed ICC networks help to facilitate an improved understanding of the mechanisms underpinning GI myoelectric activity and the diseases associated with its dysfunction.

Analysis of Regional Variations of the Interstitial Cells of Cajal in the Murine Distal Stomach Informed by Confocal Imaging and Machine Learning Methods.

Introduction: The network of Interstitial Cells of Cajal (ICC) plays a plethora of key roles in maintaining, coordinating, and regulating the contractions of the gastrointestinal (GI) smooth muscles. Several GI functional motility disorders have been associated with ICC degradation. This study extended a previously reported 2D morphological analysis and applied it to 3D spatial quantification of three different types of ICC networks in the distal stomach guided by confocal imaging and machine learning methods. The characterization of the complex changes in spatial structure of the ICC network architecture contributes to our understanding of the roles that different types of ICC may play in post-prandial physiology, pathogenesis, and/or amelioration of GI dsymotility- bridging structure and function. Methods: A validated classification method using Trainable Weka Segmentation was applied to segment the ICC from a confocal dataset of the gastric antrum of a transgenic mouse, followed by structural analysis of the segmented images. Results: The machine learning model performance was compared to manually segmented subfields, achieving an area under the receiver-operating characteristic (AUROC) of 0.973 and 0.995 for myenteric ICC (ICC-MP; n = 6) and intramuscular ICC (ICC-IM; n = 17). The myenteric layer in the distal antrum increased in thickness (from 14.5 to 34 µm) towards the lesser curvature, whereas the thickness decreased towards the lesser curvature in the proximal antrum (17.7 to 9 µm). There was an increase in ICC-MP volume from proximal to distal antrum (406,960 ± 140,040 vs. 559,990 ± 281,000 µm3; p = 0.000145). The % of ICC volume was similar for ICC-LM and for ICC-CM between proximal (3.6 ± 2.3% vs. 3.1 ± 1.2%; p = 0.185) and distal antrum (3.2 ± 3.9% vs. 2.5 ± 2.8%; p = 0.309). The average % volume of ICC-MP was significantly higher than ICC-IM at all points throughout sample (p < 0.0001). Conclusions: The segmentation and analysis methods provide a high-throughput framework of investigating the structural changes in extended ICC networks and their associated physiological functions in animal models.

Antral Variation of Murine Gastric Pacemaker Cells Informed by Confocal Imaging and Machine Learning Methods.

The Interstitial Cells of Cajal (ICC) are specialized gastrointestinal (GI) pacemaker cells that generate and actively propagate electrophysiological events called slow waves. Slow waves regulate the GI motility necessary for digestion. Several functional GI motility disorders have been associated with depletion in the ICC. In this study, a validated Fast Random Forest (FRF) classification method using Trainable WEKA Segmentation for segmenting the networks of ICC was applied to confocal microscopy images of a whole mount tissue from the distal antrum of a mouse stomach (583 × 3,376 × 133 µm3, parcellated into 24 equal image stacks). The FRF model performance was compared to 6 manually segmented subflelds and produced an area under the receiver-operating characteristic (AUROC) of 0.95. Structural variations of ICC network in the longitudinal muscle (ICC-LM) and myenteric plexus (ICC-MP) were quantified. The average volume of ICC-MP was significantly higher than ICC-LM at any point throughout the antral tissue sampled. There was a pronounced decline of up to 80% in ICC-LM (from 3,705 µm3 to 716 µm3) over a distance of 279.3 µm, that eventually diminished towards the distal antrum. However, an inverse relationship was observed in ICC-MP with an overall increase of up to 157% (from 59,100 µm3 to 151,830 µm3) over a distance of approximately 2 mm that proceeds towards the distal antrum.

Current applications of mathematical models of the interstitial cells of Cajal in the gastrointestinal tract.

The interstitial cells of Cajal (ICC) form interconnected networks throughout the gastrointestinal (GI) tract. ICC act as the pacemaker cells that initiate the rhythmic bioelectrical slow waves and intermediary between the GI musculature and nerves, both of which are critical to GI motility. Disruptions to the number of ICC and the integrity of ICC networks have been identified as a key pathophysiological mechanism in a number of clinically challenging GI disorders. The current analyses of ICC generally rely on either functional recordings taken directly from excised tissue or morphological analysis based on images of labeled ICC, where the structural-functional relationship is investigated in an associative manner rather than mechanistically. On the other hand, computational physiology has played a significant role in facilitating our understanding of a number of physiological systems in both health and disease, and investigations in the GI field are beginning to incorporate several mathematical models of the ICC. The main aim of this review is to present the major modeling advances in GI electrophysiology, in order to introduce a multi-scale framework for mathematically quantifying the functional consequences of ICC degradation at both cellular and tissue scales. The outcomes will inform future investigators utilizing modeling techniques in their studies. This article is categorized under: Metabolic Diseases > Computational Models.

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.