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

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

Inactivation of mould spores in a model system and on raisins by low-energy electron beam

Low-energy electron beam was investigated as an intervention strategy for the reduction of spores of food-spoiling fungi. Inactivation kinetics of five strains from the genera Aspergillus, Byssochlamys, Eurotium and Penicillium were determined. A two-dimensional membrane was used as a model system to exclude matrix effects. Raisins were incorporated into the study as an example of a three-dimensional food surface structure that is regularly being affected by spoilage through moulds. Complete inactivation could be attained on the model system after a maximum dose of 6 kGy, whereas between 1.04 and 1.71 log reductions were achieved on raisins. This novel application of low-energy electron beam was shown to reduce mould spores even on challenging food surfaces, paving the way for implementation in food industry

Annexin V expression on CD4⁺ T cells with regulatory function

Regulatory T (Treg) cells induce immunologic tolerance by suppressing effector functions of conventional lymphocytes in the periphery. On the other hand, immune silencing is mediated by recognition of phosphatidylserine (PS) on apoptotic cells by phagocytes. Here we describe expression of the PS-binding protein Annexin V (ANXA5) in CD4+  CD25hi Treg cells at the mRNA and protein levels. CD4+  ANXA5+ T cells constitute about 0·1%-0·6% of peripheral blood CD3+ T cells, exhibit co-expression of several Treg markers, such as Forkhead box P3, programmed cell death protein-1, cytotoxic T-lymphocyte antigen-4 and CD38. In vitro, ANXA5+ Treg cells showed enhanced adhesion to PS+ endothelial cells. Stimulated by anti-CD3 and PS+ syngeneic antigen-presenting cells CD4+  ANXA5+ T cells expanded in the absence of exogenous interleukin-2. CD4+  ANXA5+ T cells suppressed CD4+  ANXA5- T-cell proliferation and mammalian target of rapamycin phosphorylation, partially dependent on cell contact. CD4+  ANXA5+ T-cell-mediated suppression was allo-specific and accompanied by an increased production of anti-inflammatory mediators. In vivo, using a model of delayed type hypersensitivity, murine CD4+  ANXA5+ T cells inhibited T helper type 1 responses. In conclusion, we report for the first time expression of ANXA5 on a subset of Treg cells that might bridge classical regulatory Treg function with immune silencing

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

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