3D Visualization, Skeletonization and Branching Analysis of Blood Vessels in Angiogenesis

Abstract

Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role.

Morphometric Analysis of the Thymic Epithelial Cell (TEC) Network Using Integrated and Orthogonal Digital Pathology Approaches

“…In imaging, “skeletonization” transforms binary objects to one-pixel-thick representations, facilitating topological analysis and reducing dimensionality. This technique, applicable to both 2D and 3D models of any branched network 138,139 , is notably used in studying vasculature development and angiogenesis 140 . Here, we adapted an ImageJ plugin 38 , to assess topological descriptors, such as number of branches/junctions, leveraging the meshwork architecture of the cTEC network, which resembles angiogenic vasculature 4,141 .…”

Immunohistochemical Assessment of Microvessel Density in OSCC: Spatial Heterogeneity of Angiogenesis and Its Impact on Survival

“…Moreover, manual identification of hot spot regions and vessel counting by pathologists may introduce subjective bias [12]. Therefore, future research should consider sophisticated methods, such as algorithm-based identification of hotspots and vessel counting, as well as computational 3D reconstruction, to elucidate the spatial distribution of blood vessels within tumors [45,46].…”