Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

Predicting capillary vessel network hemodynamics in silico by machine learning

Blood velocity and red blood cell (RBC) distribution profiles in a capillary vessel cross-section in the microcirculation are generally complex and do not follow Poiseuille's parabolic or uniform pattern. Existing imaging techniques used to map large microvascular networks in vivo do not allow a direct measurement of full 3D velocity and RBC concentration profiles, although such information is needed for accurate evaluation of the physiological variables, such as the wall shear stress (WSS) and near-wall cell-free layer (CFL), that play critical roles in blood flow regulation, disease progression, angiogenesis, and hemostasis. Theoretical network flow models, often used for hemodynamic predictions in experimentally acquired images of the microvascular network, cannot provide the full 3D profiles either. In contrast, such information can be readily obtained from high-fidelity computational models that treat blood as a suspension of deformable RBCs. These models, however, are computationally expensive and not feasible for extension to the microvascular network at large spatial scales up to an organ level. To overcome such limitations, here we present machine learning (ML) models that bypass such expensive computations but provide highly accurate and full 3D profiles of the blood velocity, RBC concentration, WSS, and CFL in every vessel in the microvascular network. The ML models, which are based on artificial neural networks and convolution-based U-net models, predict hemodynamic quantities that compare very well against the true data but reduce the prediction time by several orders. This study therefore paves the way for ML to make detailed and accurate hemodynamic predictions in spatially large microvascular networks at an organ-scale.

Intravital Imaging of Thrombosis Models in Mice

Intravital microscopy is a powerful tool to study thrombosis in real time. The kinetics of thrombus formation and progression in vivo is studied after inflicting damage to the endothelium through mechanical, chemical, or laser injury. Mouse models of atherosclerosis are also used to induce thrombus formation. Vessels of different sizes and from different vascular beds such as carotid artery or vena cava, mesenteric or cremaster arterioles, can be targeted. Using fluorescent dyes, antibodies, or reporter mouse strains allows to visualize key cells and factors mediating the thrombotic processes. Here, we review the latest literature on using intravital microscopy to study thrombosis as well as thromboinflammation following transient middle cerebral artery occlusion, infection-induced immunothrombosis, and liver ischemia reperfusion.

Automated Stabilization, Enhancement and Capillaries Segmentation in Videocapillaroscopy

Oral capillaroscopy is a critical and non-invasive technique used to evaluate microcirculation. Its ability to observe small vessels in vivo has generated significant interest in the field. Capillaroscopy serves as an essential tool for diagnosing and prognosing various pathologies, with anatomic-pathological lesions playing a crucial role in their progression. Despite its importance, the utilization of videocapillaroscopy in the oral cavity encounters limitations due to the acquisition setup, encompassing spatial and temporal resolutions of the video camera, objective magnification, and physical probe dimensions. Moreover, the operator's influence during the acquisition process, particularly how the probe is maneuvered, further affects its effectiveness. This study aims to address these challenges and improve data reliability by developing a computerized support system for microcirculation analysis. The designed system performs stabilization, enhancement and automatic segmentation of capillaries in oral mucosal video sequences. The stabilization phase was performed by means of a method based on the coupling of seed points in a classification process. The enhancement process implemented was based on the temporal analysis of the capillaroscopic frames. Finally, an automatic segmentation phase of the capillaries was implemented with the additional objective of quantitatively assessing the signal improvement achieved through the developed techniques. Specifically, transfer learning of the renowned U-net deep network was implemented for this purpose. The proposed method underwent testing on a database with ground truth obtained from expert manual segmentation. The obtained results demonstrate an achieved Jaccard index of 90.1% and an accuracy of 96.2%, highlighting the effectiveness of the developed techniques in oral capillaroscopy. In conclusion, these promising outcomes encourage the utilization of this method to assist in the diagnosis and monitoring of conditions that impact microcirculation, such as rheumatologic or cardiovascular disorders.

Intravital microscopic observation of the microvasculature during hemodialysis in healthy rats

Hemodialysis (HD) provides life-saving treatment for kidney failure. Patient mortality is extremely high, with cardiovascular disease (CVD) being the leading cause of death. This results from both a high underlying burden of cardiovascular disease, as well as additional physiological stress from the HD procedure itself. Clinical observations indicate that HD is associated with microvascular dysfunction (MD), underlining the need for a fundamental pathophysiological assessment of the microcirculatory consequences of HD. We therefore successfully developed an experimental small animal model, that allows for a simultaneous real-time assessment of the microvasculature. Using in-house built ultra-low surface area dialyzers and miniaturized extracorporeal circuit, we successfully dialyzed male Wistar Kyoto rats and combined this with a simultaneous intravital microscopic observation of the EDL microvasculature. Our results show that even in healthy animals, a euvolemic HD procedure can induce a significant systemic hemodynamic disturbance and induce disruption of microvascular perfusion (as evidence by a reduction in the proportion of the observed microcirculation receiving blood flow). This study, using a new small animal hemodialysis model, has allowed direct demonstration that microvascular blood flow in tissue in skeletal muscle is acutely reduced during HD, potentially in concert with other microvascular beds. It shows that preclinical small animal models can be used to further investigate HD-induced ischemic organ injury and allow rapid throughput of putative interventions directed at reducing HD-induced multi-organ ischemic injury.