An analysis modality for vascular structures combining tissue-clearing technology and topological data analysis

Abstract

The blood and lymphatic vasculature networks are not yet fully understood even in mouse because of the inherent limitations of imaging systems and quantification methods. This study aims to evaluate the usefulness of the tissue-clearing technology for visualizing blood and lymphatic vessels in adult mouse. Clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC) enables us to capture the high-resolution 3D images of organ- or area-specific vascular structures. To evaluate these 3D structural images, signals are first classified from the original captured images by machine learning at pixel base. Then, these classified target signals are subjected to topological data analysis and non-homogeneous Poisson process model to extract geometric features. Consequently, the structural difference of vasculatures is successfully evaluated in mouse disease models. In conclusion, this study demonstrates the utility of CUBIC for analysis of vascular structures and presents its feasibility as an analysis modality in combination with 3D images and mathematical frameworks.

Visualization of Organ-Specific Lymphatic Growth: An Efficient Approach to Labeling Molecular Markers in Cleared Tissues

“…Conventional visualization techniques such as paraffin sections, reporter mouse models and whole-mount staining are not sufficient to visualize the lymphatic vasculature of organs due to the opacity of native tissue ( Figure 1 ) [ 15 ]. Tissue-clearing techniques address this problem by clearing pigments and matching RI-indices, creating optically transparent tissue samples that can be used for imaging, but many of the protocols have limitations that make them unsuitable for the visualization of lymphatic vessels [ 16 , 18 , 19 , 21 , 22 , 26 , 30 , 31 , 32 , 33 , 34 ]. We decided to base our protocol on a non-toxic and efficient tissue-clearing protocol named CUBIC [ 23 , 24 , 25 ].…”

“…In comparison to other CUBIC protocols [ 23 , 24 , 25 , 26 ], we could acquire images of great quality by stereo and confocal microscopy and did not need an expensive and complex LSFM ( Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 ).…”

“…CUBIC consists of two parts: the removal of lipids and pigments in the decolorization step followed by the matching of refraction indices in the RI-adjustment step, both of which result in a transparent tissue sample that can be used for imaging [ 23 , 24 , 25 ]. The latest protocols based on the original CUBIC protocol produce remarkable 3D images and use advanced quantification tools, but they require expensive light-sheet fluorescence microscopes (LSFMs) and specialized equipment as well as knowledge of machine learning and programming to quantify and analyze images [ 26 , 27 , 28 ]. The data volume of 3D images is vast and requires special computers and, more importantly, special knowledge for processing [ 24 ].…”

“…The data volume of 3D images is vast and requires special computers and, more importantly, special knowledge for processing [ 24 ]. The analysis of LSFM images is time-consuming and the analysis of a single image can take hours and even days to complete [ 24 , 26 ].…”

AI-based Solution for Improving Neuroradiology Workflow for Cerebrovascular Structure Monitoring

“…Fig. 1 shows that, while the red segmentation is not perfect, it captures the topological structure very well 8 . Whereas the green segmentation accurately segments large vessels, it fails to segment small and thin vessels.…”