A Novel Artificial Intelligence-Based Approach for Quantitative Assessment of Angiogenesis in the Ex Ovo CAM Model

Abstract

Angiogenesis is a highly regulated process. It promotes tissue regeneration and contributes to tumor growth. Existing therapeutic concepts interfere with different steps of angiogenesis. The quantification of the vasculature is of crucial importance for research on angiogenetic effects. The chorioallantoic membrane (CAM) assay is widely used in the study of angiogenesis. Ex ovo cultured chick embryos develop an easily accessible, highly vascularised membrane on the surface. Tumor xenografts can be incubated on this membrane enabling studies on cancer angiogenesis and other major hallmarks. However, there is no commonly accepted gold standard for the quantification of the vasculature of the CAM. We compared four widely used measurement techniques to identify the most appropriate one for the quantification of the vascular network of the CAM. The comparison of the different quantification methods suggested that the CAM assay application on the IKOSA platform is the most suitable image analysis application for the vasculature of the CAM. The new CAM application on the IKOSA platform turned out to be a reliable and feasible tool for practical use in angiogenesis research. This novel image analysis software enables a deeper exploration of various aspects of angiogenesis and might support future research on new anti-angiogenic strategies for cancer treatment.

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“…To study these effects, different methods of quantifying vasculature have been used and described including the manual count of the vessels. However, this is quite time-consuming and prone to human bias whereas automated and semi-automated applications enable the quantitative assessment of various vasculature morphometric and spatial parameters ( Faihs et al, 2022 ). With recent developments in artificial intelligence, new possibilities for image analysis have emerged.…”

“…With recent developments in artificial intelligence, new possibilities for image analysis have emerged. The IKOSA platform, as used in this study, facilitates AI-based analysis of vascular networks on CAM images, the underlying image analysis model is based on a fully convolutional neural network, which uses an InceptionResNetv2 as an encoder and a U-Net based structure as a decoder path ( Faihs et al, 2022 ). The analysis results in a segmentation map in terms of a binary mask representing the vessel area at pixel level.…”

“…The analysis results in a segmentation map in terms of a binary mask representing the vessel area at pixel level. The study by Faihs et al (2022) suggests that the IKOSA method is considered the most precise for analyzing vascular parameters, specifically the branching points and mean vascular thickness. This conclusion is based on a comparison with the standard manual count.…”

Editorial for Special Issue: The Chorioallantoic Membrane (CAM) Model—Traditional and State-of-the Art Applications: The 1st International CAM Conference

“…Heuberger et al reported that the potential anti-cancer effect of copper chelators was mostly mediated through angiosuppression [4]. Faihs et al introduced the application of a novel image analysis software for angiogenesis research [5].…”

The Influence of Photobiomodulation in the CAM Assay – Establishing a CAM burn wound model

“…The ex-ovo CAM model was used as describes previously. Briefely, fertilized white Lohmann chicken eggs (Schropper GmbH, Gloggnitz, Austria) were incubated for three days at a temperature of 37.6°C and constant humidity while being turned automatically in an incubator (Easy 100, J.Hemel Brutgeraete, Verl, Germany) [25]. Since the experimental conditions provide the optimal environment for the proliferation of pathogenic bacteria, all work surfaces were regularly disinfected to maintain a sterile working environment [26].…”