A three-dimensional method for morphological analysis and flow velocity estimation in microvasculature on-a-chip

Abstract

Three-dimensional (3D) imaging techniques (e.g., confocal microscopy) are commonly used to visualize in vitro models, especially microvasculature on-a-chip. Conversely, 3D analysis is not the standard method to extract quantitative information from those models. We developed the μVES algorithm to analyze vascularized in vitro models leveraging 3D data. It computes morphological parameters (geometry, diameter, length, tortuosity, eccentricity) and intravascular flow velocity. μVES application to microfluidic vascularized in vitro models shows that they successfully replicate functional features of the microvasculature in vivo in terms of intravascular fluid flow velocity. However, wall shear stress is lower compared to in vivo references. The morphological analysis also highlights the model's physiological similarities (vessel length and tortuosity) and shortcomings (vessel radius and surface-over-volume ratio). The addition of the third dimension in our analysis produced significant differences in the metrics assessed compared to 2D estimations. It enabled the computation of new indices, such as vessel eccentricity. These μVES capabilities can find application in analyses of different in vitro vascular models, as well as in vivo and ex vivo microvasculature.

Keywords: 3D computational analysis; deep learning; network morphology; segmentation; vasculature‐on‐a‐chip.

FIGURE 1

2D algorithm validation against REAVER code and manual analysis. (a) Single image comparison of the skeleton (centerlines of the vessels and junctions) with REAVER (left) and μVES code (right). The network is also presented in the μVES image for skeleton accuracy interpretation. (b) Evaluation of the radius distribution in the image and (c) the length of the network using the three methods. Agreement between the radius value (d) and the total length (e) based on the analysis of 30 images with both REAVER and μVES.

FIGURE 2

Comparison of the network reconstruction and the vessel radius and length distributions for the three types of analysis (2D, 3D DWS, 3D). The dotted lines on the graphs show average values. DWS, downsampling.

FIGURE 3

Scheme of the μVES algorithm starting from confocal imaging to the outputs: (i) image acquisition, (ii) data preprocessing including possible downsampling operations and volumetric interpolation, (iii) network segmentation with active contour method or deep learning‐based classification, (iv) vertical alignment, (v) skeletonization, (vi) branch processing identifying different branches in the network and interpolating the spatial coordinates, (vii) descriptive metrics computation, and (viii) velocity estimation. The four images on the right depict key steps of the algorithm, which are identified by a dotted line.

FIGURE 4

Deep learning segmentation versus Active contour segmentation on a single network (a) showing true positive (TP), false positive (FP), false‐negative (FN), and true negative (black). Radius and length comparison over the validation set (b) and their distribution on a single image (c). The computational time (d) was compared using the two methods with different downsampling factors (DF).

FIGURE 5

Functional velocity evaluation. The raw image of the network—perfused with FITC labeled IgG (a)—was used to generate flow rate (b), velocity (c), and wall shear stresses (d) map. The distribution of variables is shown for the velocity (e)—against in vitro measures—and for the wall shear stresses (f). Scale bar in (a) is 500 μm.